Showing papers on "Soft computing published in 2004"

Classification and Modeling with Linguistic Information Granules: Advanced Approaches to Linguistic Data Mining

Abstract: This book clearly describes how soft computing techniques like fuzzy logic, evolutionary computation and neural networks can be used for extracting interpretable knowledge in the form of linguistic if-then rules from numerical data for classification and modeling. While emphasis is placed on the interpretability of linguistic knowledge, this book covers almost all soft computing techniques for linguistic data mining.

Modeling complex systems using fuzzy cognitive maps

Abstract: This research deals with the soft computing methodology of fuzzy cognitive map (FCM). Here a mathematical description of FCM is presented and a new methodology based on fuzzy logic techniques for developing the FCM is examined. The capability and usefulness of FCM in modeling complex systems and the application of FCM to modeling and describing the behavior of a heat exchanger system is presented. The applicability of FCM to model the supervisor of complex systems is discussed and the FCM-supervisor for evaluating the performance of a system is constructed; simulation results are presented and discussed.

Soft Computing and Intelligent Systems Design: Theory, Tools and Applications

Abstract: soft computing and intelligent systems design theory tools intelligent systems and soft computing prospects tools and soft computing optimizer for intelligent control systems soft computing and intelligent systems theory and applications soft computing techniques for process control applications soft computing and fractal theory for intelligent section 3 soft computing concepts and applications intelligent control systems using soft computing tbsh intelligent systems: architectures and perspectives arxiv evolutionary fuzzy logic computation inflibnet intelligent decision and control intelligent systems ulisboa fuzzy information and engineering volume 1 advances in a s on c ntelligence techniques to data m advances in intelligent and soft computing 125 multimedia tools and applications springer advances in intelligent and soft computing 136 multi objective programming and goal programming theory fuzzy information and engineering volume 1 advances in intelligent agents and their applications studies in soft computing methods in flight control system design ingenious tools of soft computing ijesr fuzzy information and engineering volume 1 advances in international journal on soft computing, artificial soft computing techniques course code: 13ee2113 l p c step response enhancement of hybrid stepper motors using rainbow vacuum manual browserfame sony rm yd029 manual browserfame the secret deaths of arthur lowe ebook | honey963 solution manual neuro fuzzy and soft computing a review of: static and dynamic neural networks soft computing-based life-cycle cost analysis tools for information sciences elsevier empowering knowledge a comparative study on computational intelligence robert smithson mapping dislocations fakof red cross bls manual 2013 fakof 3/3 mca first semester soft computing credits : 4 vibration monitoring, testing, and instrumentation fuzzy sets in pattern recognition and machine intelligence bank haters handbook marsal verifying stability of dynamic soft-computing systems

Artificial Intelligence and Soft Computing - ICAISC 2004

Abstract: Information system design and optimum sizing is a very complex task. Theoretical research and practitioners often tackle the optimization problem by applying specific techniques for the optimization of individual design phases, usually leading to local optima. Conversely, this paper proposes the definition of a design methodology based on an evolutionary approach to the optimization of the client/server-farm distributed structure, which is typical of a distributed information technology (IT) architecture. The optimization problem consists of finding the minimum-cost physical systems that satisfy all architectural requirements given by the designer. The proposed methodology allows for the identification of the architectural solution that minimizes costs, against different information system requirements and multiple design alternatives, thorough a genetic-based exploration of the solution space. Experimental results show that costs can be significantly reduced with respect to conventional approaches adopted by IT designers and available in the professional literature.

Towards missing data imputation: A study of fuzzy k-means clustering method

Abstract: In this paper, we present a missing data imputation method based on one of the most popular techniques in Knowledge Discovery in Databases (KDD), i.e. clustering technique. We combine the clustering method with soft computing, which tends to be more tolerant of imprecision and uncertainty, and apply a fuzzy clustering algorithm to deal with incomplete data. Our experiments show that the fuzzy imputation algorithm presents better performance than the basic clustering algorithm.

Case generation using rough sets with fuzzy representation

Abstract: We propose a rough-fuzzy hybridization scheme for case generation. Fuzzy set theory is used for linguistic representation of patterns, thereby producing a fuzzy granulation of the feature space. Rough set theory is used to obtain dependency rules which model informative regions in the granulated feature space. The fuzzy membership functions corresponding to the informative regions are stored as cases along with the strength values. Case retrieval is made using a similarity measure based on these membership functions. Unlike the existing case selection methods, the cases here are cluster granules and not sample points. Also, each case involves a reduced number of relevant features. These makes the algorithm suitable for mining data sets, large both in dimension and size, due to its low-time requirement in case generation as well as retrieval. Superiority of the algorithm in terms of classification accuracy and case generation and retrieval times is demonstrated on some real-life data sets.

Fuzzy Logic Based-Map Matching Algorithm for Vehicle Navigation System in Urban Canyons

Abstract: With the rapid progress in the development of wireless technology, Global Positioning System (GPS) based vehicle navigation systems are being widely deployed in automobiles to serve the location-based needs of users and for efficient traffic management. An essential process in vehicle navigation is to map match the position obtained from GPS (or/and other sensors) on a road network map. This process of map matching in turn helps in mitigating errors from navigation solution. GPS based vehicle navigation systems have difficulties in tracking vehicles in urban canyons due to poor satellite availability. High Sensitivity GPS (HS GPS) receivers can alleviate this problem by acquiring and tracking weak signals (and increasing the availability), but at the cost of high measurement noise and errors due to multipath and cross correlation. Position and velocity results in such conditions are typically biased and have unknown distributions. Thus filtering and other statistical methods are difficult to implement. Soft computing has replaced classical computing on many fronts where uncertainties are difficult to model. Fuzzy logic, based on fuzzy reasoning concepts, is one of the most widely used soft computational methods. In many circumstances, it can take noisy, imprecise input, to yield crisp (i.e. numerically accurate) output. Fuzzy logic can be applied effectively to map match the output from a HS GPS receiver in urban canyons because of its inherent tolerance to imprecise inputs. This paper describes a map matching algorithm based on fuzzy logic. The input of the system comes from a SiRF HS XTrac GPS receiver and a low cost gyro (Murata ENV-05G). The results show an improvement in tracking the vehicle in urban canyon conditions.

A Neuro-Fuzzy Approach for Modelling Electricity Demand in Victoria

Abstract: Neuro-fuzzy systems have attracted growing interest of researchers in various scientific and engineering areas due to the increasing need of intelligent systems. This paper evaluates the use of two popular soft computing techniques and conventional statistical approach based on Box--Jenkins autoregressive integrated moving average (ARIMA) model to predict electricity demand in the State of Victoria, Australia. The soft computing methods considered are an evolving fuzzy neural network (EFuNN) and an artificial neural network (ANN) trained using scaled conjugate gradient algorithm (CGA) and backpropagation (BP) algorithm. The forecast accuracy is compared with the forecasts used by Victorian Power Exchange (VPX) and the actual energy demand. To evaluate, we considered load demand patterns for 10 consecutive months taken every 30 min for training the different prediction models. Test results show that the neuro-fuzzy system performed better than neural networks, ARIMA model and the VPX forecasts.

Soft Computing and its Applications in Business and Economics

Abstract: 1 Introduction to Soft Computing.- 1.1 Basic Concepts of Soft Computing.- 2.2 Combination of Constituents of Soft Computing.- References.- 2. Constituent Methodologies of Soft Computing.- 2.1 Elements of Fuzzy Sets Theory.- 2.1.1 Fuzzy Sets and Operations Over Them.- 2.2.2 Mathematics of Fuzzy Computing.- 2.1.3 Fuzzy Logic and Approximate Reasoning.- 2.1.4 Probability and Fuzziness.- 2.1.5 Fuzzy Sets and Possibility Theory.- 2.2 Foundations of Neurocomputing.- 2.2.1 Basic Types and Architectures of Neural Networks.- 2.2.2 Learning Algorithms of Neural Networks.- 2.3 Probabilistic Computing.- 2.3.1 Bayesian Approach.- 2.3.2 Dempster-Shafer Theory of Belief.- 2.4 Evolutionary Computing.- 2.4.1 Evolution Programming and Genetic Algorithms.- 2.4.2 Computation with Genetic Algorithms.- 2.5 Chaotic Computing.- 2.5.1 Elements of Chaotic Computing.- 2.5.2 Non-Linear Dynamics and Chaotic Analysis.- 2.5.3 Empirical Chaotic Analysis.- References.- 3. Emerging Combined Soft Computing Technologies.- 3.1 Neuro-Fuzzy Technology.- 3.2 Neuro-Genetic Approach.- 3.3 Fuzzy Genetic Paradigm.- 3.4 Genetic Algorithms with Fuzzy Logic.- 3.5 Neuro-Fuzzy-Genetic Paradigm.- 3.6 Multi-Agent Distributed Intelligent Systems Paradigm.- 3.7 Computing with Words Technology.- References.- 4. Soft Computing Technologies in Business and Economic Forecasting.- 4.1 Neuro-Computing and Forecasting.- 4.2 Fuzzy Time Series Based Forecasting.- 4.3 Fuzzy Delphi Method.- 4.4 Soft Computing Based Forecasting Complex Time Series.- 4.5 Soft Computing Based Prediction Ensemble for Forecasting in Chaotic Time Series.- References.- 5 Soft Computing Based Decision Making and DSS.- 5.1 Fuzzy Linear Programming.- 5.2 Evolutionary Algorithm Based Fuzzy Linear Programming.- 5.3 Fuzzy Chaos Approach to Fuzzy Linear Programming Problem.- 5.4 Fuzzy-Probabilistic Scheduling for Oil Refinery.- 5.5 Fuzzy Decision Making.- 5.6 Multi-Agent Distributed Intelligent System Based on Fuzzy Decision Making.- 5.7 Soft Computing and Data Mining.- 5.8 Soft Computing Based Multi-Agent Marketing DSS.- 5.9 Hybrid DSS Based on Simulation and Genetic Algorithms.- 5.10 Soft Computing Based Alternatives Generations by Decision Support Systems.- References.- 6 Soft Computing in Marketing.- 6.1 Marketing Analysis of a Customer's Purchasing Behavior.- 6.2 Customer Credit Evaluation.- 6.3 Soft Computing Based Fraud Detection.- 6.4 Fuzzy Evaluation of Service Quality.- 6.5 Application of Fuzzy Programming to Hospital's Service Performance Evaluating.- References.- 7 Soft Computing Applications in Operations Management.- 7.1 Application of Fuzzy Logic in Transportation Logistics.- 7.2 Scheduling Fuzzy Expert Systems with Probabilistic Reasoning for Oil Refineries.- 7.3 Detection and Withdrawal of Defect Parts in the Computer Aided Manufacturing of Evaporators.- 7.4 Genetic Algorithms Based Fuzzy Regression Analysis and Its Applications for Quality Evaluation.- 7.5 An Intelligent System for Diagnosis of the Oil-Refinery Plant.- 7.6 Neuro-Fuzzy Pattern Recognition in Manufacturing.- 7.7 Soft Computing Based Inventory Control.- 7.8 Fuzzy Project Scheduling.- 7.9 CW Based Decision Analysis on Risk Assessment of an Engineering Project.- References.- 8 Soft Computing in Finance.- 8.1 Soft Computing Based Stock Market Predicting System.- 8.2 Fuzzy Nonlinear Programming Approach to Portfolio Selection.- 8.3 Neuro-Fuzzy Approach to Modeling of Credit Risk in Trading Portfolios.- 8.4 A Fuzzy Approach to the Credit Portfolio Constructing.- 8.5 Soft Computing Based TDSS Multi-Agent Systems in Finance.- 8.6 Neural Nonlinear Modeling for Risk Management in Banking.- 8.7 Neuro-Fuzzy Loan Assessment System.- References.- 9 Soft Computing in Electronic Business.- 9.1 A Multi-Agent System for E-Commerce Decisions.- 9.2 Soft Computing and Personalization of Electronic Commerce.- 9.3 Risk Analysis in Electronic Commerce Using Fuzzy Weighted Average.- References.

Soft Computing Applications in Infrastructure Management

Abstract: Infrastructure management decisions, such as condition assessment, performance prediction, needs analysis, prioritization, and optimization are often based on data that is uncertain, ambiguous, and incomplete and incorporate engineering judgment and expert opinion. Soft computing techniques are particularly appropriate to support these types of decisions because these techniques are very efficient at handling imprecise, uncertain, ambiguous, incomplete, and subjective data. This paper presents a review of the application of soft computing techniques in infrastructure management. The three most used soft computing constituents, artificial neural networks, fuzzy systems, and genetic algorithms, are reviewed, and the most promising techniques for the different infrastructure management functions are identified. Based on the applications reviewed, it can be concluded that soft computing techniques provide appealing alternatives for supporting many infrastructure management functions. Although the soft computing constituents have several advantages when used individually, the development of practical and efficient intelligent tools is expected to require a synergistic integration of complementary techniques into hybrid models.

ANFIS based modelling and control of non-linear systems : a tutorial

Abstract: This work is an attempt to illustrate the utility and effectiveness of soft-computing approaches in handling the modelling and control of complex systems. Soft computing research is concerned with the integration of artificial intelligent tools (neural networks, fuzzy technology, and evolutionary algorithms) in a complementary hybrid framework for solving real world problems. The present work concentrates on the pioneering neuro-fuzzy system ANFIS (adaptive neuro fuzzy inference system). ANFIS is first used to model nonlinear knee-joint dynamics from recorded clinical data. The established model is then used to predict the behaviour of the underlying system and for the design and evaluation of various intelligent control strategies.

Data Mining: Multimedia, Soft Computing, and Bioinformatics

Abstract: Chapter 17, “Optimal Design for Experimental Inputs,” focuses on D-optimality and other mathematical criteria for optimization within a design context. The book contains five appendixes: Multivariate Analysis, Statistical Tests, Probability Theory, Random Number Generation, and Markov Processes. It also contains 256 exercises, of which 25 have (partial) solutions in the back of the book. There is an extensive set of references, containing 216 entries. The index is passable but could be improved; of 26 terms from randomly chosen chapter introductions, the index contained 21. Acronyms should be given their own index entries, but few are. The book has a website, www.jhuap.edu/ISSO, containing three datasets, errata (very few; I also found the book to be well edited), a table of contents and excerpts from the Preface, sample syllabi for the two courses, presentation slides from short courses (noncommercial instructors may use these), sample code, and related links. The site is clearly laid out. In summary, I recommend this book for a graduate course or self-study in optimization and equation solving for functions evaluated with noise. In both cases, readers may need to supplement the book with material from the references: for a course, with details of mathematical rigor, including proofs and in some cases regularity conditions, and for a reader, with more detailed application from her own field. The omission of proofs and the use of relatively simple and broadly understandable examples make for a more accessible book. I appreciated the informative discussions of issues and relative merits of procedures. In the author’s words,

Soft computing-based computational intelligent for reservoir characterization

Abstract: 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. 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. 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.

Fraud detection in electrical energy consumers using rough sets

Abstract: Rough set is an emergent technique of soft computing that have been used in many knowledge discovery in database applications. This work describes an application of rough sets in the fraud detection of electrical energy consumers. From an information system, rough sets concept of reduct was used to reduce the number of conditional attributes and the minimal decision algorithm (MDA) was used to reduce some values of conditional attributes. The reduced information system derives a set of rules that reaches consumers behavior, allowing the classification rule system to predict many fraud consumers profiles. Rough sets prove that it is a powerful technique with application in many systems based in data.

Basic Algorithms and Tools for Rough Non-deterministic Information Analysis

Abstract: Roughnon-deterministicinformation analysis is a framework for handling the rough sets based concepts, which are defined in not only DISs (DeterministicInformation Systems) but also NISs (Non-deterministicInformation Systems), on computers. NISs were proposed for dealing with information incompleteness in DISs. In this paper, two modalities, i.e., the certainty and the possibility, are defined for each concept like the definability of a set, the consistency of an object, data dependency, rule generation, reduction of attributes, criterion of rules support, accuracy and coverage. Then, each algorithm for computing two modalities is investigated. An important problem is how to compute two modalities depending upon all derived DISs. A simple method, such that two modalities are sequentially computed in all derived DISs, is not suitable. Because the number of all derived DISs increases in exponential order. This problem is uniformly solved by means of applying either inf and sup information or possibleequivalence relations. An information analysis tool for NISs is also presented.

Case-Based Reasoning: Concepts, Features and Soft Computing

Abstract: Here we first describe the concepts, components and features of CBR. The feasibility and merits of using CBR for problem solving is then explained. This is followed by a description of the relevance of soft computing tools to CBR. In particular, some of the tasks in the four REs, namely Retrieve, Reuse, Revise and Retain, of the CBR cycle that have relevance as prospective candidates for soft computing applications are explained.

Design and Application of Hybrid Intelligent Systems

Abstract: Intelligent Systems cover a broad area of knowledge-based systems, computational intelligence, soft computing, and their hybrid combinations. Research and development in intelligent systems have enabled us to not only solve a range of problems which were previously considered too difficult but also have enabled a larger number of other problems to be tackled more effectively. This volume focuses on the recent research developments on intelligent systems in a hybrid environment and its applications in business systems, image processing, Internet modeling, control/automation and data mining. The different contributions presented in this volume were accepted for the Third International Conference on Hybrid Intelligent Systems (HIS'03).

Soft Computing Models for Network Intrusion Detection Systems

Abstract: Security of computers and the networks that connect them is increasingly becoming of great significance. Computer security is defined as the protection of computing systems against threats to confidentiality, integrity, and availability. There are two types of intruders: external intruders, who are unauthorized users of the machines they attack, and internal intruders, who have permission to access the system with some restrictions. This chapter presents a soft computing approach to detect intrusions in a network. Among the several soft computing paradigms, we investigated fuzzy rule-based classifiers, decision trees, support vector machines, linear genetic programming and an ensemble method to model fast and efficient intrusion detection systems. Empirical results clearly show that soft computing approach could play a major role for intrusion detection.

Pattern Recognition Algorithms for Data Mining: Scalability, Knowledge Discovery, and Soft Granular Computing

Abstract: INTRODUCTION Introduction Pattern Recognition in Brief Knowledge Discovery in Databases (KDD) Data Mining Different Perspectives of Data Mining Scaling Pattern Recognition Algorithms to Large Data Sets Significance of Soft Computing in KDD Scope of the Book MULTISCALE DATA CONDENSATION Introduction Data Condensation Algorithms Multiscale Representation of Data Nearest Neighbor Density Estimate Multiscale Data Condensation Algorithm Experimental Results and Comparisons Summary UNSUPERVISED FEATURE SELECTION Introduction Feature Extraction Feature Selection Feature Selection Using Feature Similarity (FSFS) Feature Evaluation Indices Experimental Results and Comparisons Summary ACTIVE LEARNING USING SUPPORT VECTOR MACHINE Introduction Support Vector Machine Incremental Support Vector Learning with Multiple Points Statistical Query Model of Learning Learning Support Vectors with Statistical Queries Experimental Results and Comparison Summary ROUGH-FUZZY CASE GENERATION Introduction Soft Granular Computing Rough Sets Linguistic Representation of Patterns and Fuzzy Granulation Rough-fuzzy Case Generation Methodology Experimental Results and Comparison Summary ROUGH-FUZZY CLUSTERING Introduction Clustering Methodologies Algorithms for Clustering Large Data Sets CEMMiSTRI: Clustering using EM, Minimal Spanning Tree and Rough-fuzzy Initialization Experimental Results and Comparison Multispectral Image Segmentation Summary ROUGH SELF-ORGANIZING MAP Introduction Self-Organizing Maps (SOM) Incorporation of Rough Sets in SOM (RSOM) Rule Generation and Evaluation Experimental Results and Comparison Summary CLASSIFICATION, RULE GENERATION AND EVALUATION USING MODULAR ROUGH-FUZZY MLP Introduction Ensemble Classifiers Association Rules Classification Rules Rough-Fuzzy MLP Modular Evolution of Rough-Fuzzy MLP Rule Extraction and Quantitative Evaluation Experimental Results and Comparison Summary APPENDIX A: ROLE OF SOFT-COMPUTING TOOLS IN KDD Fuzzy Sets Neural Networks Neuro-Fuzzy Computing Genetic Algorithms Rough Sets Other Hybridizations APPENDIX B DATA SETS USED IN EXPERIMENTS

Soft data mining, computational theory of perceptions, and rough-fuzzy approach

Abstract: Data mining and knowledge discovery is described from pattern recognition point of view along with the relevance of soft computing. Key features of the computational theory of perceptions and its significance in pattern recognition and knowledge discovery problems are explained. Role of fuzzy-granulation (f-granulation) in machine and human intelligence, and its modeling through rough-fuzzy integration are discussed. Merits of fuzzy granular computation, in terms of performance and computation time, for the task of case generation in large scale case-based reasoning systems are illustrated through an example.

ANTIDS: Self-Organized Ant-based Clustering Model for Intrusion Detection System

Abstract: Security of computers and the networks that connect them is increasingly becoming of great significance. Computer security is defined as the protection of computing systems against threats to confidentiality, integrity, and availability. There are two types of intruders: the external intruders who are unauthorized users of the machines they attack, and internal intruders, who have permission to access the system with some restrictions. Due to the fact that it is more and more improbable to a system administrator to recognize and manually intervene to stop an attack, there is an increasing recognition that ID systems should have a lot to earn on following its basic principles on the behavior of complex natural systems, namely in what refers to self-organization, allowing for a real distributed and collective perception of this phenomena. With that aim in mind, the present work presents a self-organized ant colony based intrusion detection system (ANTIDS) to detect intrusions in a network infrastructure. The performance is compared among conventional soft computing paradigms like Decision Trees, Support Vector Machines and Linear Genetic Programming to model fast, online and efficient intrusion detection systems.

Soft lambda-Calculus: A Language for Polynomial Time Computation

Abstract: Soft linear logic ([Lafont02]) is a subsystem of linear logic characterizing the class PTIME. We introduce Soft lambda-calculus as a calculus typable in the intuitionistic and affine variant of this logic. We prove that the (untyped) terms of this calculus are reducible in polynomial time. We then extend the type system of Soft logic with recursive types. This allows us to consider non-standard types for representing lists. Using these datatypes we examine the concrete expressiveness of Soft lambda-calculus with the example of the insertion sort algorithm.

Optimization of extrusion blow molding processes using soft computing and Taguchi’s method

Abstract: The objective of this study is to present a new numerical strategy using soft-computing techniques to determine the optimal die gap programming of extrusion blow molding processes. In this study, the design objective is to target a uniform part thickness after parison inflation by manipulating the parison die gap openings over time. To model the whole process, that is, the parison extrusion, the mould clamping and the parison inflation, commercial finite element software (BlowSim) from the National Research Council (NRC) of Canada is used. However, the use of such software is time-consuming and one important issue in a design environment is to minimize the number of simulations to get the optimal operating conditions. To do so, we proposed a new strategy called fuzzy neural–Taguchi network with genetic algorithm (FUNTGA) that establishes a back propagation network using a Taguchi’s experimental array to predict the relationship between design variables and responses. Genetic algorithm (GA) is then applied to search for the optimum design of die gap parison programming. As the number of training samples is greatly reduced due to the use of orthogonal arrays, the prediction accuracy of the neural network model is closely related to the distance between sampling points and the evolved designs. The extrapolation distance concept is proposed and introduced to GA using fuzzy rules to modify the fitness function and thus improving search efficiency. The comparison of the results with commercial optimization software from NRC demonstrates the effectiveness of the proposed approach.