Showing papers on "Soft computing published in 2008"
01 Jan 2008
TL;DR: This chapter provides two recent algorithms for evolutionary optimization – well known as particle swarm optimization (PSO) and differential evolution (DE), inspired by biological and sociological motivations and can take care of optimality on rough, discontinuous and multimodal surfaces.
Abstract: Since the beginning of the nineteenth century, a significant evolution in optimization theory has been noticed. Classical linear programming and traditional non-linear optimization techniques such as Lagrange’s Multiplier, Bellman’s principle and Pontyagrin’s principle were prevalent until this century. Unfortunately, these derivative based optimization techniques can no longer be used to determine the optima on rough non-linear surfaces. One solution to this problem has already been put forward by the evolutionary algorithms research community. Genetic algorithm (GA), enunciated by Holland, is one such popular algorithm. This chapter provides two recent algorithms for evolutionary optimization – well known as particle swarm optimization (PSO) and differential evolution (DE). The algorithms are inspired by biological and sociological motivations and can take care of optimality on rough, discontinuous and multimodal surfaces. The chapter explores several schemes for controlling the convergence behaviors of PSO and DE by a judicious selection of their parameters. Special emphasis is given on the hybridizations of PSO and DE algorithms with other soft computing tools. The article finally discusses the mutual synergy of PSO with DE leading to a more powerful global search algorithm and its practical applications.
426 citations
TL;DR: This paper makes an attempt to apply a promising set of genetic programming techniques which are known as multi expression programming, gene expression programming (GEP) and linear genetic programming (LGP) to the uniaxial compressive strength (UCS) and tensile strength prediction of chalky and clayey soft limestone.
Abstract: Accurate determination of compressive and tensile strength of limestone is an important subject for the design of geotechnical structures. Although there are several classical approaches in the literature for strength prediction their predictive accuracy is generally not satisfactory. The trend in the literature is to apply artificial intelligence based soft computing techniques for complex prediction problems. Artificial neural networks which are a member of soft computing techniques were applied to strength prediction of several types of rocks in the literature with considerable success. Although artificial neural networks are successful in prediction, their inability to explicitly produce prediction equations can create difficulty in practical circumstances. Another member of soft computing family which is known as genetic programming can be a very useful candidate to overcome this problem. Genetic programming based approaches are not yet applied to the strength prediction of limestone. This paper makes an attempt to apply a promising set of genetic programming techniques which are known as multi expression programming (MEP), gene expression programming (GEP) and linear genetic programming (LGP) to the uniaxial compressive strength (UCS) and tensile strength prediction of chalky and clayey soft limestone. The data for strength prediction were generated experimentally in the University of Gaziantep civil engineering laboratories by using limestone samples collected from Gaziantep region of Turkey.
306 citations
01 Mar 2008
TL;DR: This paper mathematically and experimentally proves that the simultaneous consideration of randomness and opposition is more advantageous than pure randomness, and applies that to accelerate differential evolution (DE).
Abstract: For many soft computing methods, we need to generate random numbers to use either as initial estimates or during the learning and search process. Recently, results for evolutionary algorithms, reinforcement learning and neural networks have been reported which indicate that the simultaneous consideration of randomness and opposition is more advantageous than pure randomness. This new scheme, called opposition-based learning, has the apparent effect of accelerating soft computing algorithms. This paper mathematically and also experimentally proves this advantage and, as an application, applies that to accelerate differential evolution (DE). By taking advantage of random numbers and their opposites, the optimization, search or learning process in many soft computing techniques can be accelerated when there is no a priori knowledge about the solution. The mathematical proofs and the results of conducted experiments confirm each other.
303 citations
TL;DR: A new procedure to predict time series using paradigms such as: fuzzy systems, neural networks and evolutionary algorithms, so that the linear model can be identified automatically, without the need of human expert participation is presented.
203 citations
01 Jun 2008
TL;DR: Fuzzy cognitive maps are suitable for medical decision support systems and appropriate FCM architectures are proposed and developed as well as the corresponding examples from two medical disciplines, i.e. speech and language pathology and obstetrics, are described.
Abstract: Medical decision support systems can provide assistance in crucial clinical judgments, particularly for inexperienced medical professionals. Fuzzy cognitive maps (FCMs) is a soft computing technique for modeling complex systems, which follows an approach similar to human reasoning and the human decision-making process. FCMs can successfully represent knowledge and human experience, introducing concepts to represent the essential elements and the cause and effect relationships among the concepts to model the behavior of any system. Medical decision systems are complex systems that can be decomposed to non-related and related subsystems and elements, where many factors have to be taken into consideration that may be complementary, contradictory, and competitive; these factors influence each other and determine the overall clinical decision with a different degree. Thus, FCMs are suitable for medical decision support systems and appropriate FCM architectures are proposed and developed as well as the corresponding examples from two medical disciplines, i.e. speech and language pathology and obstetrics, are described.
189 citations
01 Jan 2008
TL;DR: The main advantage of the proposed FCM grading model is the sufficient interpretability and transparency in decision process, which make it a convenient consulting tool in characterizing tumor aggressiveness for every day clinical practice.
Abstract: The characterization and accurate determination of brain tumor grade is very important because it influences and specifies patient's treatment planning and eventually his life. A new method for characterizing brain tumors is presented in this research work, which models the human thinking approach and the classification results are compared with other computational intelligent techniques proving the efficiency of the proposed methodology. The novelty of the method is based on the use of the soft computing method of fuzzy cognitive maps (FCMs) to represent and model experts' knowledge (experience, expertise, heuristic). The FCM grading model classification ability was enhanced introducing a computational intelligent training technique, the Activation Hebbian Algorithm. The proposed method was validated for clinical material, comprising of 100 cases. FCM grading model achieved a diagnostic output of accuracy of 90.26% (37/41) and 93.22% (55/59) for brain tumors of low-grade and high-grade, respectively. The results of the proposed grading model present reasonably high accuracy, and are comparable with existing algorithms, such as decision trees and fuzzy decision trees which were tested at the same type of initial data. The main advantage of the proposed FCM grading model is the sufficient interpretability and transparency in decision process, which make it a convenient consulting tool in characterizing tumor aggressiveness for every day clinical practice.
179 citations
01 Jan 2008
TL;DR: It is demonstrated that the ensemble is able to yield lower Type I and Type II errors compared to its constituent models and outperformed an earlier study that used multivariate discriminant analysis (MDA), MLFF-BP and human judgment.
Abstract: This paper presents a soft computing based bank performance prediction system. It is an ensemble system whose constituent models are a multi-layered feed forward neural network trained with backpropagation (MLFF-BP), a probabilistic neural network (PNN) and a radial basis function neural network (RBFN), support vector machine (SVM), classification and regression trees (CART) and a fuzzy rule based classifier. Further, principal component analysis (PCA) based hybrid neural networks, viz. PCA-MLFF-BP, PCA-PNN and PCA-RBF are also included as constituents of the ensemble. Moreover, GRNN and PNN were trained with a genetic algorithm to optimize the smoothing factors. Two ensembles (i) simple majority voting based and (ii) weightage based are implemented. This system predicts the performance of a bank in the coming financial year based on its previous 2-years' financial data. Ten-fold cross-validation is performed in the training sessions and results are validated with an independent production set. It is demonstrated that the ensemble is able to yield lower Type I and Type II errors compared to its constituent models. Further, the ensemble also outperformed an earlier study [P.G. Swicegood, Predicting poor bank profitability: a comparison of neural network, discriminant analysis and professional human judgement, Ph.D. Thesis, Department of Finance, Florida State University, 1998] that used multivariate discriminant analysis (MDA), MLFF-BP and human judgment.
170 citations
TL;DR: The proposed merged MISO FNN with RGA (FNNRGA) can achieve faster convergence and lower estimation error than neural networks with the back propagation method and the overfitting suppression features are significantly improved.
Abstract: In this paper, a soft computing technique for estimating battery state-of-charge of individual batteries in a battery string is proposed. The soft computing approach uses a fusion of a fuzzy neural network (FNN) with B-spline membership functions (BMFs) and a reduced-form genetic algorithm (RGA). The algorithm is employed to tune both control points of the BMFs and the weights of the FNNs. The traditional multiple-input multiple-output FNN (MIMOFNN) cannot directly be used in this paper. The main reason is that there are too many free parameters in the MIMOFNN to be trained if many inputs are required. In this paper, a merged multiple-input single-output (MISO) FNN is proposed and can be trained by the RGA optimization approach. The merged MISO FNN with RGA (FNNRGA) can achieve faster convergence and lower estimation error than neural networks with the back propagation method. From experimental results, the proposed merged MISO FNNRGA is superior, more robust than the traditional method, and the overfitting suppression features are significantly improved.
133 citations
Book•
20 Aug 2008
TL;DR: This book is an introduction to some new fields in soft computing with its principal components of fuzzy logic, ANN and EA and it is hoped that it would be quite useful to study the fundamental concepts on these topics for the pursuit of allied research.
Abstract: Intuitive consciousness/ wisdom is also one of the frontline areas in soft computing, which has to be always cultivated by meditation. This book is an introduction to some new fields in soft computing with its principal components of fuzzy logic, ANN and EA and it is hoped that it would be quite useful to study the fundamental concepts on these topics for the pursuit of allied research. The approach in this book is to provides an understanding of the soft computing field, to work through soft computing using examples, to integrate pseudo - code operational summaries and Matlab codes, to present computer simulation, to include real world applications and to highlight the distinctive work of human consciousness in machine. "I believe the chapters would help in understanding not only the basic issues and characteristic features of soft computing, but also the aforesaid problems of CTP and in formulating possible solutions. Dr. Chaturvedi deserves congratulations for bringing out the nice piece of work." Sankar K. Pal, Director Indian Statistical Institute
130 citations
TL;DR: A flexible decision support system to help managers in their decision-making functions that simulates experts’ evaluations using ordered weighted average (OWA) aggregation operators, which assign different weights to different selection criteria.
TL;DR: In this article, ensemble models are developed to accurately forecast software reliability, including statistical (multiple linear regression and multivariate adaptive regression splines) and intelligent techniques (backpropagation trained neural network, dynamic evolving neuro-fuzzy inference system and TreeNet).
TL;DR: This paper shows how fuzzy transform can be used for detection and characterization of dependencies among attributes and applies it to mining associations that have a functional character.
TL;DR: F fuzzy logic, genetic algorithms and artificial neural networks, as well as their fusion are reviewed in order to examine the capability of soft computing methods and techniques to effectively address various hard-to-solve design tasks and issues.
01 Jan 2008
TL;DR: This study used a Hopfield neural network to allocate the given apparatuses and equipment to the bearing plate surfaces in the satellite module, and integrated genetic algorithm/particle swarm optimization (GA/PSO) and quasi-principal component analysis (QPCA) to deal with the further detailed layout optimization.
Abstract: This paper presents a hybrid method using soft computing techniques to deal with layout design problem of a satellite module. This problem is a three-dimensional layout optimization problem with behavioral constraints, and is difficult to solve in polynomial time. In this study, we firstly used a Hopfield neural network (HNN) to allocate the given apparatuses and equipment to the bearing plate surfaces in the satellite module. Then, we integrated genetic algorithm/particle swarm optimization (GA/PSO) and quasi-principal component analysis (QPCA) to deal with the further detailed layout optimization. The numerical experimental results showed the feasibility and efficiency of our method for layout optimization of a satellite module.
24 Nov 2008-World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering
TL;DR: This work develops a correlation-based feature selection algorithm to remove the worthless information from the original high dimensional database and designs an intrusion detection method to solve the problems of uncertainty caused by limited and ambiguous information.
Abstract: The network traffic data provided for the design of intrusion detection always are large with ineffective information and enclose limited and ambiguous information about users’ activities. We study the problems and propose a two phases approach in our intrusion detection design. In the first phase, we develop a correlation-based feature selection algorithm to remove the worthless information from the original high dimensional database. Next, we design an intrusion detection method to solve the problems of uncertainty caused by limited and ambiguous information. In the experiments, we choose six UCI databases and DARPA KDD99 intrusion detection data set as our evaluation tools. Empirical studies indicate that our feature selection algorithm is capable of reducing the size of data set. Our intrusion detection method achieves a better performance than those of participating intrusion detectors. Keywords—Intrusion detection, feature selection, k-nearest neighbors, fuzzy clustering, Dempster-Shafer theory
01 Jan 2008
TL;DR: The parameters of PID controller are tuned for controlling the armature controlled DC motor.
Abstract: Summary PID controllers are widely used in industrial plants because it is simple and robust. Industrial processes are subjected to variation in parameters and parameter perturbations, which when significant makes the system unstable. So the control engineers are on look for automatic tuning procedures. In this paper, the parameters of PID controller are tuned for controlling the armature controlled DC motor. Continuous cycling method & ZN step response method are the conventional methods whose performance have been compared and analyzed with the intelligent tuning techniques like Genetic algorithm, Evolutionary programming and particle swarm optimization. GA, EP and PSO based tuning methods have proved their excellence in giving better results by improving the steady state characteristics and performance indices.
TL;DR: The role played until now by these main soft computing ingredients is analyzed, and then an original proposal of the new constituents, mainly focused on the introduction of the broader topic of metaheuristics instead of evolutionary algorithms, is justified, presented and described.
01 Jan 2008
TL;DR: This chapter provides a comprehensive survey of the existing main linguistic aggregation operators, and briefly discusses their characteristics and applications.
Abstract: Linguistic aggregation operators are a powerful tool to aggregate linguistic information, which have been studied and applied in a wide variety of areas, including engineering, decision making, artificial intelligence, data mining, and soft computing. In this chapter, we provide a comprehensive survey of the existing main linguistic aggregation operators, and briefly discuss their characteristics and applications. Finally, we roughly classify all these linguistic aggregation operators and conclude with a discussion of some interesting further research directions.
22 Dec 2008
TL;DR: This book highlights current research and applications, addresses issues encountered in the development of applied systems, and describes a wide range of intelligent systems techniques, including neural networks, fuzzy logic, evolutionary strategy, and genetic algorithms.
Abstract: Providing a thorough introduction to the field of soft computing techniques, Intelligent Systems: Modeling, Optimization, and Control covers every major technique in artificial intelligence in a clear and practical style. This book highlights current research and applications, addresses issues encountered in the development of applied systems, and describes a wide range of intelligent systems techniques, including neural networks, fuzzy logic, evolutionary strategy, and genetic algorithms. The book demonstrates concepts through simulation examples and practical experimental results. Case studies are also presented from each field to facilitate understanding.
01 Sep 2008
TL;DR: This book gathers contributions to the 4th International Conference on Soft methods in Probability and Statistics to establish a dialogue between fuzzy random variables and imprecise probability theories.
Abstract: Probability theory has been the only well-founded theory of uncertainty for a long time. It was viewed either as a powerful tool for modelling random phenomena, or as a rational approach to the notion of degree of belief. During the last thirty years, in areas centered around decision theory, artificial intelligence and information processing, numerous approaches extending or orthogonal to the existing theory of probability and mathematical statistics have come to the front. The common feature of those attempts is to allow for softer or wider frameworks for taking into account the incompleteness or imprecision of information. Many of these approaches come down to blending interval or fuzzy interval analysis with probabilistic methods. This book gathers contributions to the 4th International Conference on Soft methods in Probability and Statistics. Its aim is to present recent results illustrating such new trends that enlarge the statistical and uncertainty modeling traditions, towards the handling of incomplete or subjective information. It covers a broad scope ranging from philosophical and mathematical underpinnings of new uncertainty theories, with a stress on their impact in the area of statistics and data analysis, to numerical methods and applications to environmental risk analysis and mechanical engineering. A unique feature of this collection is to establish a dialogue between fuzzy random variables and imprecise probability theories.
TL;DR: An adaptive multi-model sliding mode controller for robotic manipulators by using the multiple models technique, the nominal part of the control signal is constructed according to the most appropriate model at different environments by using adaptive single-input-single-output fuzzy systems or radial basis function (RBF) neural networks.
TL;DR: Two different neural network based schemes for fault diagnosis based on the nonlinear behaviour of the system being diagnosed as well as the robustness of a fault diagnosis scheme with respect to modelling uncertainty are described.
Abstract: Challenging design problems arise regularly in modern fault diagnosis systems. Unfortunately, classical analytical techniques often cannot provide acceptable solutions to such difficult tasks. This explains why soft computing techniques such as neural networks become more and more popular in industrial applications of fault diagnosis. Taking into account the two crucial aspects, i.e., the nonlinear behaviour of the system being diagnosed as well as the robustness of a fault diagnosis scheme with respect to modelling uncertainty, two different neural network based schemes are described and carefully discussed. The final part of the paper presents an illustrative example regarding the modelling and fault diagnosis of a DC motor, which shows the performance of the proposed strategy.
01 Jun 2008
TL;DR: The use of Soft Computing Techniques is explored to build a suitable model structure to utilize improved estimations of software effort for NASA software projects to tune the parameters of the famous COnstructive COst MOdel (COCOMO).
Abstract: Accurate estimation of the software effort and schedule affects the budget computation. Bidding for contracts depends mainly on the estimated cost. Inaccurate estimates will lead to failure of making a profit, increased probability of project incompletion and delay of the project delivery date. In this paper, we explore the use of Soft Computing Techniques to build a suitable model structure to utilize improved estimations of software effort for NASA software projects. In doing so, we plan to use Particle Swarm Optimization (PSO) to tune the parameters of the famous COnstructive COst MOdel (COCOMO). We plan also to explore the advantages of Fuzzy Logic to build a set of linear models over the domain of possible software Line Of Code (LOC). The performance of the developed model was evaluated using NASA software projects data set [1]. A comparison between COCOMO tuned-PSO, Fuzzy Logic (FL), Halstead, Walston-Felix, Bailey-Basili and Doty models were provided.
TL;DR: The combination of Bayesian networks, which enables an intuitive representation of the mechanisms that govern the relationships between the users, and the Fuzzy Set Theory, enabling the system to represent ambiguity or vagueness in the description of the ratings, improves the accuracy of the system.
TL;DR: The performance of SVR was found to be better than ANFIS for the data sets used and the results are helpful in understanding the relationship of machine conditions, the corresponding indicating features, the level of damage/degradation and their progression.
Abstract: This paper presents a system for monitoring and prognostics of machine conditions using soft computing (SC) techniques. The machine condition is assessed through a suitable 'monitoring index' extracted from the vibration signals. The progression of the monitoring index is predicted using an SC technique, namely adaptive neuro-fuzzy inference system (ANFIS). Comparison with a machine learning method, namely support vector regression (SVR), is also presented. The proposed prediction procedures have been evaluated through benchmark data sets. The prognostic effectiveness of the techniques has been illustrated through previously published data on several types of faults in machines. The performance of SVR was found to be better than ANFIS for the data sets used. The results are helpful in understanding the relationship of machine conditions, the corresponding indicating features, the level of damage/degradation and their progression.
10 Oct 2008
TL;DR: It's important for you to start having that hobby that will lead you to join in better concept of life, and reading will be a positive activity to do every time.
Abstract: soft computing for hybrid intelligent systems What to say and what to do when mostly your friends love reading? Are you the one that don't have such hobby? So, it's important for you to start having that hobby. You know, reading is not the force. We're sure that reading will lead you to join in better concept of life. Reading will be a positive activity to do every time. And do you know our friends become fans of soft computing for hybrid intelligent systems as the best book to read? Yeah, it's neither an obligation nor order. It is the referred book that will not make you feel disappointed.
TL;DR: An evaluation of the two intelligent controllers based on fuzzy logic and artificial neural network designed for performing a wall-following task is presented, comparing them in terms of hardware resource requirements, operational speeds, and trajectory tracking errors in following different predefined trajectories.
Abstract: Soft computing techniques are generally well suited for vehicular control systems that are usually modeled by highly nonlinear differential equations and working in unstructured environments. To demonstrate their applicability in real-world applications, two intelligent controllers based on fuzzy logic and artificial neural network are designed for performing a wall-following task. Based on performance and flexibility considerations, the two controllers are implemented onto a reconfigurable hardware platform, namely a field-programmable gate array. As comparative studies of these two embedded hardware controllers designed for the same vehicular application are limited in literature, this research also presents an evaluation of the two controllers, comparing them in terms of hardware resource requirements, operational speeds, and trajectory tracking errors in following different predefined trajectories.
Book•
01 Jan 2008
TL;DR: 25 studies consider such topics as parallel and distributed architectures and biologically inspired computing, neural networks for identifying nonlinear systems, the knowledge- based adaptation of a neurofuzzy model in the predictive control of a heat exchanger, and toward intelligent machines.
Abstract: From the Publisher:
Invited researchers from around the world discuss the principles of and their experience with soft computing, an emerging discipline rooted in a group of technologies that aim to exploit the tolerance for imprecision and uncertainty in achieving solutions to complex problems. Within the broad categories of foundations, theory, implications and applications, and future prospects, the 25 studies consider such topics as parallel and distributed architectures and biologically inspired computing, neural networks for identifying nonlinear systems, the knowledge- based adaptation of a neurofuzzy model in the predictive control of a heat exchanger, and toward intelligent machines. The field is so new that the bibliography claims comprehension.
TL;DR: A feed forward neural network (NN), a radial basis function network (RBF) and a trained fuzzy algorithm are compared for regional yield estimation of agricultural crops (winter rye, winter barley).
Abstract: In this paper two areas of soft computing (fuzzy modeling and artificial neural networks) are discussed. Based on the fundamental mathematical similarity of fuzzy techniques and radial basis function networks a new training algorithm for fuzzy models is introduced. A feed forward neural network (NN), a radial basis function network (RBF) and a trained fuzzy algorithm are compared for regional yield estimation of agricultural crops (winter rye, winter barley). As training pattern a data set from a training region (Maerkisch-Oderland district, Germany) and as test pattern a data set from a three times larger region were used. Specific advantages and disadvantages of these methods for the estimation of yield were discussed.