Showing papers on "Neuro-fuzzy published in 2005"

From soft sets to information systems

Abstract: This paper discusses the relationship between soft sets and information systems. It is shown that soft sets are a class of special information systems. After soft sets are extended to several classes of general cases, the more general results also show that partition-type soft sets and information systems have the same formal structures, and that fuzzy soft sets and fuzzy information systems are equivalent.

Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications

Abstract: Part I: Introduction Computational Intelligence: An Introduction Traditional Problem Definition Part II: Basic Intelligent Computational Technologies Neural Networks Approach Fuzzy Logic Approach Evolutionary Computation Part III: Hybrid Computational Technologies Neuro-fuzzy Approach Transparent Fuzzy/Neuro-fuzzy Modeling Evolving Neural and Fuzzy Systems Adaptive Genetic Algorithms Part IV: Recent Developments The State of the Art and Development Trends

Fuzzy Mathematical Programming and Fuzzy Matrix Games

Abstract: Crisp matrix and bi-matrix games: some basic results.- Fuzzy sets.- Fuzzy numbers and fuzzy arithmetic.- Linear and quadratic programming under fuzzy environment.- Duality in linear and quadratic programming under fuzzy environment.- Matrix games with fuzzy goals.- Matrix games with fuzzy pay-offs.- More on matrix games with fuzzy pay-offs.- Fuzzy Bi-Matrix Games.- Modality and other approaches for fuzzy linear programming.

Stochastic stability analysis of fuzzy hopfield neural networks with time-varying delays

Abstract: The ordinary Takagi-Sugeno (TS) fuzzy models have provided an approach to represent complex nonlinear systems to a set of linear sub-models by using fuzzy sets and fuzzy reasoning. In this paper, stochastic fuzzy Hopfield neural networks with time-varying delays (SFVDHNNs) are studied. The model of SFVDHNN is first established as a modified TS fuzzy model in which the consequent parts are composed of a set of stochastic Hopfield neural networks with time-varying delays. Secondly, the global exponential stability in the mean square for SFVDHNN is studied by using the Lyapunov-Krasovskii approach. Stability criterion is derived in terms of linear matrix inequalities (LMIs), which can be effectively solved by some standard numerical packages.

Hybridization of fuzzy GBML approaches for pattern classification problems

Abstract: We propose a hybrid algorithm of two fuzzy genetics-based machine learning approaches (i.e., Michigan and Pittsburgh) for designing fuzzy rule-based classification systems. First, we examine the search ability of each approach to efficiently find fuzzy rule-based systems with high classification accuracy. It is clearly demonstrated that each approach has its own advantages and disadvantages. Next, we combine these two approaches into a single hybrid algorithm. Our hybrid algorithm is based on the Pittsburgh approach where a set of fuzzy rules is handled as an individual. Genetic operations for generating new fuzzy rules in the Michigan approach are utilized as a kind of heuristic mutation for partially modifying each rule set. Then, we compare our hybrid algorithm with the Michigan and Pittsburgh approaches. Experimental results show that our hybrid algorithm has higher search ability. The necessity of a heuristic specification method of antecedent fuzzy sets is also demonstrated by computational experiments on high-dimensional problems. Finally, we examine the generalization ability of fuzzy rule-based classification systems designed by our hybrid algorithm.

Fuzzy Controller Design: Theory and Applications

Abstract: The book presents clear, practical, easy-to-use methods for designing and implementing fuzzy control systems It addresses the heuristic nature of fuzzy controller design and methods to overcome the resulting problems It examines the design of hybrid, adaptive, and self-learning fuzzy control structures It explains original concepts and methods such as phase-plane-based initial presetting, sensitivity model-based self-organization, and PLC-based implementation It provides easy-to-follow worked examples in MATLAB The final chapter explores industrial applications with emphasis on techniques for fuzzy controller implementation and different implementation platforms for various applications

Fuzzy modeling and genetic algorithms for data mining and exploration

Abstract: Preface Acknowledgements Introduction PART ONE CONCEPTS AND ISSUES Chapter 1. Foundations and Ideas Chapter 2. Principal Model Types Chapter 3. Approaches to Model Building PART TWO FUZZY SYSTEMS Chapter 4. Fundamental Concepts of Fuzzy Logic Chapter 5. Fundamental Concepts of Fuzzy Systems Chapter 6. FuzzySQL and Intelligent Queries Chapter 7. Fuzzy Clustering Chapter 8. Fuzzy Rule Induction PART THREE EVOLUTIONARY STRATEGIES Chapter 9. Fundamental Concepts of Genetic Algorithms Chapter 10. Genetic Resource Scheduling Optimization Chapter 11. Genetic Tuning of Fuzzy Models

Adaptation of Fuzzy Inference System Using Neural Learning

Abstract: The integration of neural networks and fuzzy inference systems could be formulated into three main categories: cooperative, concurrent and integrated neuro-fuzzy models. We present three different types of cooperative neurofuzzy models namely fuzzy associative memories, fuzzy rule extraction using self-organizing maps and systems capable of learning fuzzy set parameters. Different Mamdani and Takagi-Sugeno type integrated neuro-fuzzy systems are further introduced with a focus on some of the salient features and advantages of the different types of integrated neuro-fuzzy models that have been evolved during the last decade. Some discussions and conclusions are also provided towards the end of the chapter.

Supervisory control of fuzzy discrete event systems: a formal approach

Abstract: Fuzzy discrete event systems (DESs) were proposed recently by Lin and Ying, which may better cope with the real-world problems of fuzziness, impreciseness, and subjectivity such as those in biomedicine. As a continuation of, in this paper, we further develop fuzzy DESs by dealing with supervisory control of fuzzy DESs. More specifically: 1) we reformulate the parallel composition of crisp DESs, and then define the parallel composition of fuzzy DESs that is equivalent to that in . Max-product and max-min automata for modeling fuzzy DESs are considered, 2) we deal with a number of fundamental problems regarding supervisory control of fuzzy DESs, particularly demonstrate controllability theorem and nonblocking controllability theorem of fuzzy DESs, and thus, present the conditions for the existence of supervisors in fuzzy DESs; 3) we analyze the complexity for presenting a uniform criterion to test the fuzzy controllability condition of fuzzy DESs modeled by max-product automata; in particular, we present in detail a general computing method for checking whether or not the fuzzy controllability condition holds, if max-min automata are used to model fuzzy DESs, and by means of this method we can search for all possible fuzzy states reachable from initial fuzzy state in max-min automata. Also, we introduce the fuzzy n-controllability condition for some practical problems, and 4) a number of examples serving to illustrate the applications of the derived results and methods are described; some basic properties related to supervisory control of fuzzy DESs are investigated. To conclude, some related issues are raised for further consideration.

Water level forecasting through fuzzy logic and artificial neural network approaches

Abstract: . In this study three data-driven water level forecasting models are presented and discussed. One is based on the artificial neural networks approach, while the other two are based on the Mamdani and the Takagi-Sugeno fuzzy logic approaches, respectively. All of them are parameterised with reference to flood events alone, where water levels are higher than a selected threshold. The analysis of the three models is performed by using the same input and output variables. However, in order to evaluate their capability to deal with different levels of information, two different input sets are considered. The former is characterized by significant spatial and time aggregated rainfall information, while the latter considers rainfall information more distributed in space and time. The analysis is made with great attention to the reliability and accuracy of each model, with reference to the Reno river at Casalecchio di Reno (Bologna, Italy). It is shown that the two models based on the fuzzy logic approaches perform better when the physical phenomena considered are synthesised by both a limited number of variables and IF-THEN logic statements, while the ANN approach increases its performance when more detailed information is used. As regards the reliability aspect, it is shown that the models based on the fuzzy logic approaches may fail unexpectedly to forecast the water levels, in the sense that in the testing phase, some input combinations are not recognised by the rule system and thus no forecasting is performed. This problem does not occur in the ANN approach.

An improved robust fuzzy-PID controller with optimal fuzzy reasoning

Abstract: Many fuzzy control schemes used in industrial practice today are based on some simplified fuzzy reasoning methods, which are simple but at the expense of losing robustness, missing fuzzy characteristics, and having inconsistent inference. The concept of optimal fuzzy reasoning is introduced in this paper to overcome these shortcomings. The main advantage is that an integration of the optimal fuzzy reasoning with a PID control structure will generate a new type of fuzzy-PID control schemes with inherent optimal-tuning features for both local optimal performance and global tracking robustness. This new fuzzy-PID controller is then analyzed quantitatively and compared with other existing fuzzy-PID control methods. Both analytical and numerical studies clearly show the improved robustness of the new fuzzy-PID controller.

Stability analysis of fuzzy control systems subject to uncertain grades of membership

Abstract: This paper presents relaxed stability conditions for fuzzy control systems subject to parameter uncertainties. As the parameter uncertainties introduce uncertain grades of membership to the fuzzy control systems, the favorable property offered by sharing the same premises in the fuzzy plant models and fuzzy controllers cannot be employed to enhance the stabilization ability of the fuzzy control systems. To widen the applicability of the fuzzy control approach, fuzzy control systems subject to uncertain grades of membership will be investigated. New relaxed stability conditions will be derived to guarantee the stability of this class of fuzzy control systems. A numerical example will be given to show the effectiveness of the proposed approach.

Genetic Fuzzy Systems: Status, Critical Considerations and Future Directions

Abstract: Fuzzy Systems have shown their utility for solving a wide range of problems in different application domains. The use of Genetic Algorithms for designing Fuzzy Systems allows us to introduce the learning and adaptation capabilities. This topic has attracted considerable attention in the Computation Intelligence community. This short paper briefly reviews the classical models and the most recent trends for Genetic Fuzzy Systems. We pay special attention to a short discussion on some critical considerations of recent developments and to the suggestion of potential research future directions.

FLM to select suitable maintenance strategy in process industries using MISO model

Abstract: Purpose – To help the maintenance managers/decision makers to select a suitable maintenance strategy for the components/parts associated with the system.Design/methodology/approach – An approach based on fuzzy linguistic modeling is used to select the most effective and efficient maintenance strategy. Three input parameters, i.e. historical data (I1), present data (I2) and competence of data (I3) related to failures of a component (gears), were taken to judge the effectiveness of the nature of maintenance strategies. These parameters are represented as members of a fuzzy set, combined by matching them against (if‐then) rules in rule base, evaluated in fuzzy inference system (Mamdani, min‐max type) and then defuzzified to assess the capability or effectiveness of maintenance strategy.Findings – The results show how the fuzzy logic approach translates vague, ambiguous, qualitative and imprecise information into numerical/quantitative terms, which helps to identify the most informative and efficient maintena...

Optimal reconfiguration of radial distribution systems using a fuzzy mutated genetic algorithm

Abstract: A new method based on a fuzzy mutated genetic algorithm for optimal reconfiguration of radial distribution systems (RDS) is presented. The proposed algorithm overcomes the combinatorial nature of the reconfiguration problem and deals with noncontinuous multi-objective optimization. The attractive features of the algorithm are: preservation of radial property of the network without islanding any load point by an elegant coding scheme and an efficient convergence characteristic attributed to a controlled mutation using fuzzy logic.

Fuzzy rule interpolation for multidimensional input spaces with applications: a case study

Abstract: Fuzzy rule based systems have been very popular in many engineering applications. However, when generating fuzzy rules from the available information, this may result in a sparse fuzzy rule base. Fuzzy rule interpolation techniques have been established to solve the problems encountered in processing sparse fuzzy rule bases. In most engineering applications, the use of more than one input variable is common, however, the majority of the fuzzy rule interpolation techniques only present detailed analysis to one input variable case. This paper investigates characteristics of two selected fuzzy rule interpolation techniques for multidimensional input spaces and proposes an improved fuzzy rule interpolation technique to handle multidimensional input spaces. The three methods are compared by means of application examples in the field of petroleum engineering and mineral processing. The results show that the proposed fuzzy rule interpolation technique for multidimensional input spaces can be used in engineering applications.

A new hybrid method using evolutionary algorithms to train Fuzzy Cognitive Maps

Abstract: A novel hybrid method based on evolutionary computation techniques is presented in this paper for training Fuzzy Cognitive Maps. Fuzzy Cognitive Maps is a soft computing technique for modeling complex systems, which combines the synergistic theories of neural networks and fuzzy logic. The methodology of developing Fuzzy Cognitive Maps relies on human expert experience and knowledge, but still exhibits weaknesses in utilization of learning methods and algorithmic background. For this purpose, we investigate a coupling of differential evolution algorithm and unsupervised Hebbian learning algorithm, using both the global search capabilities of Evolutionary strategies and the effectiveness of the nonlinear Hebbian learning rule. The use of differential evolution algorithm is related to the concept of evolution of a number of individuals from generation to generation and that of nonlinear Hebbian rule to the concept of adaptation to the environment by learning. The hybrid algorithm is introduced, presented and applied successfully in real-world problems, from chemical industry and medicine. Experimental results suggest that the hybrid strategy is capable to train FCM effectively leading the system to desired states and determining an appropriate weight matrix for each specific problem.

Fuzzy Expert Systems

Abstract: In this chapter, the steps necessary to develop a fuzzy expert system (FES) from the initial model design through to final system evaluation will be presented The current state-of-the-art of fuzzy modelling can be summed up informally as “anything goes” What this actually means is that the developer of the fuzzy model is faced with many steps in the process each with many options from which selections must be made In general, there is no specific or prescriptive method that can be used to make these choices, there are simply heuristics (“rules-of-thumb”) which may be employed to help guide the process Each of the steps will be described in detail, a summary of the main options available will be provided and the available heuristics to guide selection will be reviewed The steps will be illustrated by describing two cases studies: one will be a mock example of a fuzzy expert system for financial forecasting and the other will be a real example of a fuzzy expert system for a medical application The expert system framework considered here is restricted to rule-based systems While there are other frameworks that have been proposed for processing information utilising fuzzy methodologies, these are generally less popular in the context of fuzzy expert systems As a note on terminology, the term model is used to refer to the abstract conception of the process being studied and hence fuzzy model is the notional representation of the process in terms of fuzzy variables, rules and methods that together define the input-output mapping relationship In contrast, the term system (as in fuzzy expert system) is used to refer to the embodiment, realisation or implementation of the theoretical model in some software language or package A single model may be realised in different forms, for example, via differing software languages or differing hardware platforms Thus it should be realised that there is a subtle, but important, distinction between the evaluation of a fuzzy model of expertise and the evaluation of (one or more of) its corresponding fuzzy expert systems A model may be evaluated as accurately capturing or representing the domain problem under consideration, whereas