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Showing papers on "Soft computing published in 2000"


Journal ArticleDOI
TL;DR: This article proposes to bring the various neuro-fuzzy models used for rule generation under a unified soft computing framework, and includes both rule extraction and rule refinement in the broader perspective of rule generation.
Abstract: The present article is a novel attempt in providing an exhaustive survey of neuro-fuzzy rule generation algorithms. Rule generation from artificial neural networks is gaining in popularity in recent times due to its capability of providing some insight to the user about the symbolic knowledge embedded within the network. Fuzzy sets are an aid in providing this information in a more human comprehensible or natural form, and can handle uncertainties at various levels. The neuro-fuzzy approach, symbiotically combining the merits of connectionist and fuzzy approaches, constitutes a key component of soft computing at this stage. To date, there has been no detailed and integrated categorization of the various neuro-fuzzy models used for rule generation. We propose to bring these together under a unified soft computing framework. Moreover, we include both rule extraction and rule refinement in the broader perspective of rule generation. Rules learned and generated for fuzzy reasoning and fuzzy control are also considered from this wider viewpoint. Models are grouped on the basis of their level of neuro-fuzzy synthesis. Use of other soft computing tools like genetic algorithms and rough sets are emphasized. Rule generation from fuzzy knowledge-based networks, which initially encode some crude domain knowledge, are found to result in more refined rules. Finally, real-life application to medical diagnosis is provided.

726 citations


BookDOI
01 Dec 2000
TL;DR: Intelligent Control Systems explores recent advances in the field from both the theoretical and the practical viewpoints and integrates intelligent control design methodologies to give designers a set of flexible, robust controllers and providestudents with a tool for solving the examples and exercises within the book.
Abstract: From the Publisher: In recent years, intelligent control has emerged as one of the most active and fruitful areas of research and development. Until now, however, there has been no comprehensive text that explores the subject with focus on the design and analysis of biological and industrial applications. Intelligent Control Systems Using Soft Computing Methodologies does all that and more. Beginning with an overview of intelligent control methodologies, the contributors present the fundamentals of neural networks, supervised and unsupervised learning, and recurrent networks. They address various implementation issues, then explore design and verification of neural networks for a variety of applications, including medicine, biology, digital signal processing, object recognition, computer networking, desalination technology, and oil refinery and chemical processes.The focus then shifts to fuzzy logic, with a review of the fundamental and theoretical aspects, discussion of implementation issues, and examples of applications, including control of autonomous underwater vehicles, navigation of space vehicles, image processing, robotics, and energy management systems. The book concludes with the integration of genetic algorithms into the paradigm of soft computing methodologies, including several more industrial examples, implementation issues, and open problems and open problems related to intelligent control technology.Suited as both a textbook and a reference, Intelligent Control Systems explores recent advances in the field from both the theoretical and the practical viewpoints. It also integrates intelligent control design methodologies to give designers a set of flexible, robust controllers and providestudents with a tool for solving the examples and exercises within the book.

252 citations


Journal ArticleDOI
TL;DR: The paper discusses the properties of several methods of combining quantitative and qualitative system information and their practical value for fault diagnosis of real process systems.

155 citations


BookDOI
01 Oct 2000
TL;DR: A tutorial on Case Based Reasoning and Fuzzy Logic and an Object-Oriented Case-Schema for Structuring Case Bases and their Application to Fashion Footwear Design.
Abstract: Preface.- 1. A Tutorial on Case-Based Reasoning.- Fuzzy Sets 2. On the Notion of Similarity in Case-Based Reasoning and Fuzzy Theory.- 3. Formalizing Case-Based Inference Using Fuzzy Rules.- Artificial Neural Networks 4. Hybrid Approaches for Integrating Neural Networks and Case-Based Reasoning.- 5. Towards Integration of Memory Based Learning and Neural Networks.- Genetic Algorithms 6. A Genetic Algorithm and a Growing Cell Structures Approach to Learning Case Retrieval Structures.- 7. An Architecture for Hybrid Creative Reasoning.- 8. Teacher: A Genetics-Based System for Learning and Generalizing Heuristics.- Neuro-Fuzzy Computing 9. Fuzzy Logic-Based Neural Network for Case-Based Reasoning.- 10. Case-Based Systems: A Neuro-Fuzzy Method for Selecting Cases.- 11. Neural-Fuzzy Approach for Maintaining Case Bases.- 12. A Neuro-Fuzzy Methodology for Case Retrieval and an Object-Oriented Case-Schema for Structuring Case Bases and their Application to Fashion Footwear Design.- Applications 13. Adaptation of Cases for Case-Based Forecasting with Neural Network Support.- 14. Armchair Mission to Mars: Using Case Based Reasoning and Fuzzy Logic to Simulate a Time Series Model of Astronaut Crew.- 15. Applications of Soft CBR at General Electric.

144 citations


Journal ArticleDOI
TL;DR: The paper proposes a framework for the development of soft computing-based controllers in modern greenhouses by applying artificial intelligence (AI) techniques to the modeling and control of some climate variables within a greenhouse.
Abstract: The methodology proposed in the paper applies artificial intelligence (AI) techniques to the modeling and control of some climate variables within a greenhouse. The nonlinear physical phenomena governing the dynamics of temperature and humidity in such systems are, in fact, difficult to model and control using traditional techniques. The paper proposes a framework for the development of soft computing-based controllers in modern greenhouses.

104 citations


Book
01 Jan 2000
TL;DR: The application of soft computing techniques can be of help to obtain greater flexibility in IR systems to model the concept of "partially intrinsic" in the IR process and to make the systems adaptive, i.e. able to "learn" the user's concept of relevance.
Abstract: Information retrieval (IR) aims at defining systems able to provide a fast and effective content-based access to a large amount of stored information. The aim of an IR system is to estimate the relevance of documents to users' information needs, expressed by means of a query. This is a very difficult and complex task, since it is pervaded with imprecision and uncertainty. Most of the existing IR systems offer a very simple model of IR, which privileges efficiency at the expense of effectiveness. A promising direction to increase the effectiveness of IR is to model the concept of "partially intrinsic" in the IR process and to make the systems adaptive, i.e. able to "learn" the user's concept of relevance. To this aim, the application of soft computing techniques can be of help to obtain greater flexibility in IR systems.

99 citations


Journal ArticleDOI
TL;DR: Three dominant hybrid approaches to intelligent control are experimentally applied to address various robotic control issues which are currently under investigation at the NASA Center for Autonomous Control Engineering.

97 citations


BookDOI
01 Jan 2000
TL;DR: This paper will present the state of the art research on the application of artificial intelligence and statistical techniques for handling the missing data problem.
Abstract: The research has developed a fuzzy logic approach to handling missing data. A prototype fuzzy model was developed, using the FuzzyTech software, to assess the quality of the steel production in terms of composition, time, and temperature. As tools like FuzzyTech are not able to handle missing data, the research has introduced a fuzzy logic approach to decision making with less data. A number of workshops were carried out in the plant, and the aired experts's knowledge was the basis for the research's development. This paper will present the state of the art research on the application of artificial intelligence and statistical techniques for handling the missing data problem

88 citations


Journal ArticleDOI
TL;DR: Describes a way of designing a hybrid decision support system in soft computing paradigm for detecting the different stages of cervical cancer using rough set theory and the Interactive Dichotomizer 3 (ID3) algorithm.
Abstract: Describes a way of designing a hybrid decision support system in soft computing paradigm for detecting the different stages of cervical cancer. Hybridization includes the evolution of knowledge-based subnetwork modules with genetic algorithms (CIAs) using rough set theory and the Interactive Dichotomizer 3 (ID3) algorithm. Crude subnetworks obtained via rough set theory and the ID3 algorithm are evolved using CAs. The evolution uses a restricted mutation operator which utilizes the knowledge of the modular structure, already generated, for faster convergence. The CA tunes the network weights and structure simultaneously. The aforesaid integration enhances the performance in terms of classification score, network size and training time, as compared to the conventional multilayer perceptron. This methodology also helps in imposing a structure on the weights, which results in a network more suitable for extraction of logical rules and human interpretation of the inferencing procedure.

79 citations


Proceedings ArticleDOI
19 Jul 2000
TL;DR: Fuzzy cognitive maps and similar soft computing techniques may contribute to the development of more sophisticated systems.
Abstract: This paper describes a soft computing technique for modelling and controlling systems: fuzzy cognitive maps (FCM). The description, representation and models of FCM are examined in detail. A FCM model is proposed, its characteristics and advantages are presented, and a development algorithm is described. Fuzzy cognitive maps and similar soft computing techniques may contribute to the development of more sophisticated systems.

78 citations


BookDOI
01 Jan 2000
TL;DR: K. Kundu: Soft Computing and Image Analysis: Features, Relevance and Hybridization.- Preprocessing and Feature Extraction: F.Russo: Image Filtering Using Evolutionary Neural Fuzzy Systems.- T. Law, D. Shibata, T. Nakamura, L. Itoh: Edge Extraction Using Fuzzed Reasoning.- S.K. Ghosh: Knowledge Reuse Mechanisms for Categorizing Related Image Sets.
Abstract: S.K. Pal, A. Ghosh, M.K. Kundu: Soft Computing and Image Analysis: Features, Relevance and Hybridization.- Preprocessing and Feature Extraction: F.Russo: Image Filtering Using Evolutionary Neural Fuzzy Systems.- T. Law, D. Shibata, T. Nakamura, L. He, H. Itoh: Edge Extraction Using Fuzzy Reasoning.- S.K. Mitra, C.A. Murthy, M.K. Kundu: Image Compression and Edge Extraction Using Fractal Technique and Genetic Algorithms.- S. Mitra, R. Castellanos, S.-Y. Yang, S. Pemmaraju: Adaptive Clustering for Efficient Segmentation and Vector Quantization of Images.- B. Uma Shankar, A. Ghosh, S.K. Pal: On Fuzzy Thresholding of Remotely Sensed Images.- W. Skarbek: Image Compression Using Pixel Neural Networks.- L He, Y. Chao, T. Nakamura, H. Itho: Genetic Algorithm and Fuzzy Reasoning for Digital Image Compression Using Triangular Plane Patches.- N B. Karayiannis, T.C. Wang: Compression of Digital Mammograms Using Wavelets and Fuzzy Algorithms for Learning Vector Quantization.- V.D. Gesu: Soft Computing and Image Analysis.- J.H. Han, T.Y. Kim, L.T. Koczy: Fuzzy Interpretation of Image Data.- Classification: M. Grabisch: New Pattern Recognition Tools Based on Fuzzy Logic for Image Understanding.- N.K. Kasabov, S.I. Israel, B.J. Woodford: Adaptive, Evolving, Hybrid Connectionist Systems for Image Pattern Recognition.- P.A. Stadter, N.K Bose: Neuro-Fuzzy Computing: Structure, Performance Measure and Applications.- K. D. Bollacker, J. Ghosh: Knowledge Reuse Mechanisms for Categorizing Related Image Sets.- K. C. Gowda, P. Nagabhushan, H.N. Srikanta Prakash: Symbolic Data Analysis for Image Processing.- Applications: N.M. Nasrabadi, S. De, L.-C. Wang, S. Rizvi, A. Chan: The Use of Artificial Neural Networks for Automatic Target Recognition.- S. Gutta, H. Wechsler:Hybrid Systems for Facial Analysis and Processing Tasks.- V. Susheela Devi, M. Narasimha Murty: Handwritten Digit Recognition Using Soft Computing Tools.- T.L. Huntsburger, J.R. Rose, D. Girard: Neural Systems for Motion Analysis: Single Neuron and Network Approaches.- H.M. Kim, B. Kosko: Motion Estimation and Compensation with Neural Fuzzy Systems.


Journal ArticleDOI
TL;DR: Basic ideas of rough set theory are presented and some possible intelligent industrial applications outlined and some may be of extreme importance in the future are outlined.
Abstract: Application of intelligent methods in industry become a very challenging issue nowadays and will be of extreme importance in the future Intelligent methods include fuzzy sets, neural networks, genetics algorithms, and other techniques known as soft computing No doubt, rough set theory can also contribute essentially to this domain In this paper, basic ideas of rough set theory are presented and some possible intelligent industrial applications outlined

Journal ArticleDOI
01 Aug 2000
TL;DR: How ensembles of recurrent neural networks can overcome some of the limitations encountered in these early prototypes are described, and an example involving the identification of anomalous events in a PWR 900 MW nuclear power plant is given.
Abstract: Any action taken on a process, for example in response to an abnormal situation or in reaction to unsafe conditions, relies on the ability to identify the state of operation or the events that are occurring. Although there might be hundreds or even thousands of measurements in a process, there are generally few events occurring. The data from these measurements must then be mapped into appropriate descriptions of the occurring event(s), which in most cases is a difficult task. A systematic study was carried out with the aim of comparing alternative neural network designs and models for performing this mapping task. Four main approaches have been investigated. Radial basis function (RBF) neural networks and cascade-RBF neural networks combined with fuzzy clustering, self-organizing map neural networks, and recurrent neural networks. The main evaluation criteria adopted were identification accuracy, reliability (i.e., correct recognition of an unknown event as such), robustness (to noise and to changing initial conditions), and real-time performance. Additionally, in this paper we describe how ensembles of recurrent neural networks can overcome some of the limitations encountered in these early prototypes, and give an example involving the identification of anomalous events in a PWR 900 MW nuclear power plant.

Book ChapterDOI
01 Oct 2000
TL;DR: A novel approach to the combination of a case based reasoning system and an artificial neural network is presented in which the Neural network is integrated within the case based Reasoning cycle so that its generalizing ability may be harnessed to provide improved case adaptation performance.
Abstract: A novel approach to the combination of a case based reasoning system and an artificial neural network is presented in which the neural network is integrated within the case based reasoning cycle so that its generalizing ability may be harnessed to provide improved case adaptation performance. The ensuing hybrid system has been applied to the task of oceanographic forecasting in a real-time environment and has produced very promising results. After presenting classifications of hybrid artificial intelligence problem-solving methods, the particular combination of case based reasoning and neural networks, as a problem-solving strategy, is discussed in greater depth. The hybrid artificial intelligence forecasting model is then explained and the experimental results obtained from trials at sea are outlined.

Proceedings ArticleDOI
07 May 2000
TL;DR: A novel technique for financial forecasting derived from a fuzzy association induction algorithm is presented, allowing the development of an evolving rule based expert system that is continuously taken into account as time progresses and thus the rulebase does not become outdated.
Abstract: The past decade has witnessed significant growth in developing intelligent tools for financial forecasting. Expert systems were quickly shown to be inadequate for the tasks required in financial forecasting due to their static nature. As a result, interest started to move towards soft computing despite the fact that comprehensibility is often of paramount concern in financial forecasting. Merging the domains of fuzzy logic and rule induction paved the way for the emergence of successful generalisation techniques with high comprehensibility. In this paper, we present a novel technique for financial forecasting derived from a fuzzy association induction algorithm, allowing the development of an evolving rule based expert system. In such a way, changing market dynamics are continuously taken into account as time progresses and thus the rulebase does not become outdated. Simulations carried out show promising results for this approach.

Journal ArticleDOI
TL;DR: A messy coding scheme for fuzzy rules reduces the size and complexity of the rule base, with the result, that the fuzzy control design remains tractable for the genetic algorithm.

01 Jan 2000
TL;DR: New lower bounds on synchronizer performance are derived, and the use of a fuzzy logic-based approach in phase synchronization for a multiple-phase-shift-keying (M-PSK) communications system is demonstrated.
Abstract: The use of soft computing (SC) techniques in communications phase synchronization provides an alternative to analytical or hard computing methods. A review of current literature indicates that the application of soft computing to communications phase synchronization is in its infancy. In this work we derive new lower bounds on synchronizer performance, and demonstrate the use of a fuzzy logic-based approach in phase synchronization for a multiple-phase-shift-keying (M-PSK) communications system. Maximum Likelihood (ML) Estimation theory provides a basis for the most widely used phase estimation schemes. In this context, a review of ML statistical communication phase estimation theory and ad-hoc techniques for M-PSK modulation is presented first. New lower bounds on phase estimator performance are derived. These new Cramer-Rao Bounds (CRBs) provide the lower bound on which to judge the performance of any estimator for M-PSK modulation with a random phase model. Computational complexities and non-linear systems that result from statistical estimation theory for M-PSK systems are also addressed. Next the fundamentals of the principle constituents of soft computing, namely fuzzy logic and neural networks, are reviewed. The fundamentals of a hybrid soft computing technique known as Adaptive Neural Fuzzy Inference System (ANFIS) are discussed. Recent advances in communications that utilize soft computing approaches to phase synchronization are reviewed. Finally, the ANFIS approach is applied to phase synchronization for M-PSK. It is shown that the ANFIS approach overcomes many of the limitations of the Mamdani and statistical approaches. A comparison of statistical and ANFIS estimator performance is presented.

Proceedings Article
01 Jan 2000
TL;DR: A new EMG pattern classification method based on soft computing techniques is proposed to help the disabled and the elderly handle rehabilitation robotic arm systems and shows that EMG is more useful than existing input devices in view of naturality, extensibility, and applicability.
Abstract: A new EMG pattern classification method based on soft computing techniques is proposed to help the disabled and the elderly handle rehabilitation robotic arm systems. First, it is shown that EMG is more useful than existing input devices, such as voice, a laser pointer, and a keypad in view of naturality, extensibility, and applicability. Next, through soft computing techniques, such as the fuzzy logic and rough set theory, a new procedure is proposed to select an essential feature set of EMG signals that is independent of users. In order to classify pre-defined motions, a fuzzy pattern classification and fuzzy min-max neural networks (FMMNN) are adopted to handle the selected minimal feature set in systematical ways. As results, motions are recognized with success rates of 83 percent and 90 percent for fuzzy pattern classification and FMMNN, respectively.

Journal ArticleDOI
TL;DR: This paper investigates the identification of nonlinear systems by utilizing soft-computing approaches and feedforward neural network architecture, radial basis function neural networks, Runge–Kutta neural networks and adaptive neuro-fuzzy inference systems are studied.

01 Jan 2000
TL;DR: This paper aims at classifying state--of-the-art intelligent systems, which have evolved over the past decade in the HIS community, and some theoretical concepts of ANN, FL and Global Optimization Algorithms namely GA, SA and TS are presented.
Abstract: The emerging need for Hybrid Intelligent Systems (HIS) is currently motivating important research and development work The integration of different learning and adaptation techniques, to overcome individual limitations and achieve synergetic effects through hybridization or fusion of these techniques, has in recent years contributed to a large number of new intelligent system designs Soft Computing (SC) introduced by Lotfi Zadeh [1] is an innovative approach to construct computationally intelligent hybrid systems consisting of Artificial Neural Network (ANN), Fuzzy Logic (FL), approximate reasoning and derivative free optimization methods such as Genetic Algorithm (GA), Simulated Annealing (SA) and Tabu Search (TS) Most of these approaches, however, follow an ad hoc design methodology, further justified by success in certain application domains Due to the lack of a common framework it remains often difficult to compare the various hybrid systems conceptually and evaluate their performance comparatively It has been over a decade since HIS were first applied to solve complicated problems In this paper, we first aim at classifying state--of--the--art intelligent systems, which have evolved over the past decade in the HIS community Some theoretical concepts of ANN, FL and Global Optimization Algorithms (GOA) namely GA, SA and TS are also presented We further attempt to summarize the work that has been done and present the current standing of our vision on HIS and future research directions

Proceedings ArticleDOI
24 Jul 2000
TL;DR: This paper shall try to utilize neural networks and GA to construct an intelligent decision support system for dealing Tokyo Stock Exchange Prices Indexes (TOPIX).
Abstract: In recent years, soft computing techniques such as neural networks, GA etc. have been successfully applied for constructing various intelligent decision support systems. In this paper, we shall try to utilize neural networks and GA to construct an intelligent decision support system for dealing Tokyo Stock Exchange Prices Indexes (TOPIX).

Proceedings ArticleDOI
01 Jan 2000
TL;DR: The next generation of reservoir characterization tools for the new millennium – soft computing is proposed and the unique roles of the three major methodologies of soft computing – neurocomputing, fuzzy logic and evolutionary computing are outlined.
Abstract: This paper presents an overview of soft computing techniques for reservoir characterization. The key techniques include neurocomputing, fuzzy logic and evolutionary computing. A number of documented studies show that these intelligent techniques are good candidates for seismic data processing and characterization, well logging, reservoir mapping and engineering. Future research should focus on the integration of data and disciplinary knowledge for improving our understanding of reservoir data and reducing our prediction uncertainty. Introduction Accurate prediction of reservoir performance is a difficult problem. This is mainly due to the failure of our understanding of the spatial distribution of lithofacies and petrophysical properties. Because of this, the recovery factors in many reservoirs are unacceptably low. The current technologies based on conventional methodologies are inadequate and/or inefficient. In this paper, we propose the next generation of reservoir characterization tools for the new millennium – soft computing. Reservoir characterization plays a crucial role in modern reservoir management. It helps to make sound reservoir decisions and improves the asset value of the oil and gas companies. It maximizes integration of multi-disciplinary data and knowledge and improves the reliability of the reservoir predictions. The ultimate product is a reservoir model with realistic tolerance for imprecision and uncertainty. Soft computing aims to exploit such a tolerance for solving practical problems. Soft computing is an ensemble of various intelligent computing methodologies which include neurocomputing, fuzzy logic and evolutionary computing. Unlike the conventional or hard computing, it is tolerant of imprecision, uncertainty and partial truth. It is also tractable, robust, efficient and inexpensive. In reservoir characterization, these intelligent techniques can be used for uncertainty analysis, risk assessment, data fusion and data mining which are applicable to feature extraction from seismic attributes, well logging, reservoir mapping and engineering. Figure 1 shows schematically the flow of information and techniques to be used for intelligent reservoir characterization. The main goal is to integrate soft data such as geological data with hard data such as 3D seismic and production data to build a reservoir and stratigraphic model. While some individual methodologies (esp. neurocomputing) have gained much popularity during the past few years, the true benefit of soft computing lies on the integration of its constituent methodologies rather than use in isolation. This paper firstly outlines the unique roles of the three major methodologies of soft computing – neurocomputing, fuzzy logic and evolutionary computing. We will summarize a number of relevant and documented reservoir characterization applications. Lastly we will provide a list of recommendations for the future use of soft computing. This includes the hybrid of various methodologies (e.g. neural-fuzzy or neuro-fuzzy, neural-genetic, fuzzy-genetic and neural-fuzzy-genetic) and the latest tool of “computing with words” (CW). CW provides a completely new insight into computing with imprecise, qualitative and linguistic phrases and is a potential tool for geological modeling which is based on words rather than exact numbers. An appendix is also provided for introducing the basics in soft computing. Neurocomputing Neurocomputing represents general computation with the use of artificial neural networks. An artificial neural network is a computer model that attempts to mimic simple biological learning processes and simulate specific functions of human nervous system. It is an adaptive, parallel information processing system which is able to develop associations, transformations or mappings between objects or data. It is also SPE 59397 Soft Computing for Intelligent Reservoir Characterization D. Tamhane, SPE, P.M. Wong, SPE, University of New South Wales, F. Aminzadeh, SPE, FACT Inc. & dGB-USA, M. Nikravesh, SPE, Energy and Geoscience Institute (EGI)-University of Utah & Zadeh Institute of Information Technology. SPE 59397 SOFT COMPUTING FOR INTELLIGENT RESERVOIR CHARACTERIZATION 2 the most popular intelligent technique for pattern recognition to date. The basic elements of a neural network are the neurons and their connection strengths (weights). Given a topology of the network structure expressing how the neurons (the processing elements) are connected, a learning algorithm takes an initial model with some “prior” connection weights (usually random numbers) and produces a final model by numerical iterations. Hence “learning” implies the derivation of the “posterior” connection weights when a performance criterion is matched (e.g. the mean square error is below a certain tolerance value). Learning can be performed by “supervised” or “unsupervised” algorithm. The former requires a set of known input-output data patterns (or training patterns), while the latter requires only the input patterns. Figure 2 depicts a typical structure of a neural network, showing three layers of neurons. The lines represent how the neurons are connected. Each line is represented by a weight value. In this case, the inputs are passed to each layer and the results are obtained at the output layer. This is commonly known as the feedforward model, in which no lateral or backward connections are used. The full technical details can be found in Bishop. Applications. The major applications of neurocomputing are seismic data processing and interpretation, well logging and reservoir mapping and engineering. Good quality seismic data is essential for realistic delineation of reservoir structures. Seismic data quality depends largely on the efficiency of data processing. The processing step is time consuming and complex. The major applications include first arrival picking, noise elimination, structural mapping, horizon picking and event tracking. A detailed review can be found in Nikravesh and Aminzadeh. For interwell characterization, neural networks have been used to derive reservoir properties by crosswell seismic data. In Chawathé et al., the authors used a neural network to relate five seismic attributes (amplitude, reflection strength, phase, frequency and quadrature) to gamma ray (GR) logs obtained at two wells in the Sulimar Queen field (Chaves County). Then the GR response was predicted between the wells and was subsequently converted to porosity based on a field-specific porosity-GR transform. The results provided good delineation of various lithofacies. Feature extraction from 3D seismic attributes is an extremely important area. Most statistical methods are failed due to the inherent complexity and nonlinear information content. Figure 3 shows an example use of neural networks for segmenting seismic characters thus deducing information on the seismic facies and reservoir properties (lithology, porosity, fluid saturation and sand thickness). A display of the level of confidence (degree of match) between the seismic character at a given point versus the representative wavelets (centers of clusters) is also shown. Combining this information with the seismic model derived from the well logs while perturbing for different properties gives physical meaning of different clusters. Monson and Pita applied neural networks to find relationships between 3D seismic attributes and well logs. The study provided realistic prediction of log responses far away from the wellbore. Boadu also used similar technology to relate seismic attributes to rock properties for sandstones. In Nikravesh et al., the author applied a combination of k-means clustering, neural networks and fuzzy c-means (a clustering algorithm in which each data vector belongs to each of the clusters to a degree specified by a membership grade) techniques to characterize a field that produces from the Ellenburger Dolomite. The techniques were used to perform clustering of 3D seismic attributes and to establish relationships between the clusters and the production log. The production log was established away from wellbore. The production log and the clusters were then superimposed at each point of a 3D seismic cube. They also identified the optimum locations for new wells based on the connectivity, size and shape of the clusters related to the pay zones (see Figure 4). The use of neural networks in well logging has been popular for nearly one decade. Many successful applications have been documented. The most recent work by Bruce et al. presented a state-of-the-art review of the use of neural networks for predicting permeability from well logs. In this application, the network is used as a nonlinear regression tool to develop transformation between well logs and core permeability. Such a transformation can be used for estimating permeability in uncored intervals and wells. One example is shown in Figure 5. In this work, the permeability profile was predicted by a Bayesian neural network. The network was trained by a training set with four well logs (GR, NPHI, RHOB and RT) and core permeability. The network also provided a measure of confidence (the standard deviation of a Gaussian function): the higher the standard deviation (“sigma”), the lower the prediction reliability. This is very useful for understanding the risk of data extrapolation. The same tool can be applied to estimate porosity and fluid saturations. Another important application is the clustering of well logs for the recognition of lithofacies. This provides useful information for improved petrophysical estimates and well correlation. Neurocomputing has also been applied to reservoir mapping. In Wong et al. and Wang et al., the authors applied a radial basis function neural network to relate the conceptual distribution of geological facies (in the form of hand drawings) to reservoir porosity. It is able to incorporate the general property trend provided

BookDOI
01 Jan 2000
TL;DR: This work focuses on the application of Neural Networks in Reactor Diagnosis and Monotoring, and the design of Reactor Controller Design Using Genetic Algorithms with Simulated Annealing in Nuclear Power Plants.
Abstract: D. Ruan: Preface.- R. Hampel, W. Kastner, A. Fenske, B. Vandreier, S. Schefter: Analysis of Selected Structures for Model-Based Measuring Methods Using Fuzzy Logic.- B. Soo Moon: A Set of Fuzzy Systems to Automate the Manual Procedures for Reactor Power Level Changes.- M.S. Fodil, F. Guely, P. Siarry, J-L. Tyran: A Fuzzy Controller for the Real Time Supervision of Nuclear Power Reactors.- D. Ruan, X.Z. Li, G. Van den Eynde: Adaptive Fuzzy Control for a Simulation of Hydraulic Analogy of a Nuclear Reactor.- J.S. Benitez-Read, D. Velez-Diaz: Controlling Neutron Power of a TRIGA Mark III Research Nuclear Reactor with Fuzzy Adaptation of the Set of Output Membership Functions.- I. Petruzela: NPP Operator Support in Decision Making - Diagnostics of the Operation Failures Using Fuzzy Logic.- J.Y. Yang, K.J. Lee: Optimal Operation Planning of Radioactive Waste Processing System by Fuzzy Theory.- P. Kunsch, A. Fiordaliso, P. Fortemps: A Fuzzy Inference System for the Economic Calculus in Radioactive Waste Management.- M.G. Na: Neuro-Fuzzy Control Applications in Pressurized Water Reactors.- D. Roverso: Neural and Fuzzy Transient Classification Systems: General Techniques and Applications in Nuclear Power Plants.- R. Govil: Neural Networks in Signal Processing.- N.S. Garis, P. Linden: Application of Neural Networks in Reactor Diagnosis and Monotoring.- J.W. Hines, A.V. Gribok, I. Attieh, R.E. Uhrig: Regularization Methods for Inferential Sensing in Nuclear Power Plants.- C.M.N.A. Pereira, R. Schirru, A.S. Martinez: Genetic Algorithms Applied to Nuclear Reactor Design Optimization.- R. Schirru, C.M.N.A. Pereira, A.S. Martinez: Genetic Algorithms Applied to the Nuclear Power Plant Operation.- K. Erkan, E. Butun: Reactor Controller Design Using Genetic Algorithms with Simulated Annealing.- Y.-S. Hu, M. Modarres: Logic-Based Hierarchies for Modeling Behavior of Complex Dynamic Systems with Applications.- A. Zardecki: Continued Fractions in Time Series Forecasting.- A.G. Huizing , F.C.A. Groen: A Possibilistic Approach to Target Classification.- E. Nissan, A. Galperin, J. Zhao, B. Knight, A. Soper: From FUELCON to FUELGEN: Tools for Fuel Reload Pattern Design.- J. Reifman, T.Y.C. Wei: Diagnosis of Unanticipated Plant Component Faults in a Portable Expert System.

Book
06 Sep 2000
TL;DR: Techniques drawn from the disciplines of fuzzy logic, neural networks, genetic algorithms and other constituents of soft computing are used throughout the book.
Abstract: This book is a collection of articles on the technologies needed for the construction of intelligent and information systems, and particularly on the role of uncertainty. The articles, written by some of the world's leading experts, cover the management of uncertainty and the modeling of intelligent and information systems. Issues related to optimization and learning in the face of uncertainty are discussed. Some of the articles describe applications to data fusion, document retrieval, risk management and nuclear plant control. Applications to the automation of human sensory skills are also presented. Techniques drawn from the disciplines of fuzzy logic, neural networks, genetic algorithms and other constituents of soft computing are used throughout the book.

Book
31 Aug 2000
TL;DR: This chapter discusses Fril - A Support Logic Programming Environment and its Applications, as well as Fuzzy Set Theory, Probability Theory, and more.
Abstract: Note to the Reader. Foreword. Preface. Part I: 1. Knowledge Discovery. Part II: 2. Knowledge Representation. 3. Fuzzy Set Theory. 4. Fuzzy Logic. 5. Probability Theory. 6. Fril - A Support Logic Programming Environment. Part III: 7. Machine Learning. Part IV: 8. Cartesian Granule Features. 9. Learning Cartesian Granule Feature Models. Part V: 10. Analysis of Cartesian Granule Feature Models. 11. Applications. Appendix: Evolutionary Computation. Glossary of Main Symbols. Subject Index.

Book
26 Jul 2000
TL;DR: The Intelligent Assistant and the YPA - An Assistant for Classified Directory Enquiries, and Machine Interpretation of Facial Expressions and Modelling Preferred Work Plans are studied.
Abstract: Section 1 - Prospects- From Computing with Numbers to Computing with Words-From Manipulation of Measurements to Manipulation of Perceptions- Bringing AI and Soft Computing Together: A Neurobiological Perspective- Future Directions for Soft Computing- Problems and Prospects in Fuzzy Data Analysis- Soft Agent Computing: Towards Enhancing Agent Technology with Soft Computing- Section 2 - Tools- NEFCLASS-J - A JAVA-Based Soft Computing Tool- Soft Computing for Intelligent Knowledge-Based Systems- Advanced Fuzzy Clustering and Decision Tree Plug-Ins for DataEngineTM- Section 3 - Applications- The Intelligent Assistant: An Overview- The YPA - An Assistant for Classified Directory Enquiries- Intelligent Multimodal User Interface- Communication Management: E-Mail and Telephone Assistants- Time Management in the Intelligent Assistant- Machine Interpretation of Facial Expressions- Modelling Preferred Work Plans

Journal ArticleDOI
TL;DR: This study proposes a more systematic method in defining the structure of a soft computing technique, namely the backpropagation neural network, when used as a controller for rubbertuator robot systems.

Proceedings ArticleDOI
07 May 2000
TL;DR: A framework that combines these three disciplines to exploit their own advantages in dealing with real world problems is proposed, a logic-based one in which class and object properties are represented by clauses.
Abstract: Logic programming, object-oriented programming and soft computing have provided advantageous methodologies and techniques for computer-based problem solving. This paper proposes a framework that combines these three disciplines to exploit their own advantages in dealing with real world problems. The framework is a logic-based one in which class and object properties are represented by clauses. Vague data in properties are represented by fuzzy sets interpreted as possibility distributions. Uncertain applicability of a property to a class or an object is represented either by a support pair defining probability lower and upper bounds, or by a certainty lower bound. Fundamental issues of uncertain membership and inheritance are then discussed and solutions to them are proposed. The result forms a basis for development of soft computing object-oriented programming systems.

Book ChapterDOI
TL;DR: A neurorough hybrid algorithm has been proposed in which rough set concepts are used for finding an initial subset of efficient features followed by a neural stage to find out the ultimate best feature subset.
Abstract: Feature subset selection is of prime importance in pattern classification, machine learning and data mining applications. Though statistical techniques are well developed and mathematically sound, they are inappropriate for dealing real world cognitive problems containing imprecise and ambiguous information. Soft computing tools like artificial neural network, genetic algorithm fuzzy logic, rough set theory and their integration in developing hybrid algorithms for handling real life problems are recently found to be the most effective. In this worka neurorough hybrid algorithm has been proposed in which rough set concepts are used for finding an initial subset of efficient features followed by a neural stage to find out the ultimate best feature subset. The reduction of original feature set results in a smaller structure and quicker learning of the neural stage and as a whole the hybrid algorithm seems to provide better performance than any algorithm from individual paradigm as is evident from the simulation results.