Showing papers on "Neuro-fuzzy published in 2000"

Analysis and synthesis of nonlinear time-delay systems via fuzzy control approach

Abstract: Takagi-Sugeno (TS) fuzzy models (1985, 1992) can provide an effective representation of complex nonlinear systems in terms of fuzzy sets and fuzzy reasoning applied to a set of linear input/output (I/O) submodels. In this paper, the TS fuzzy model approach is extended to the stability analysis and control design for both continuous and discrete-time nonlinear systems with time delay. The TS fuzzy models with time delay are presented and the stability conditions are derived using Lyapunov-Krasovskii approach. We also present a stabilization approach for nonlinear time-delay systems through fuzzy state feedback and fuzzy observer-based controller. Sufficient conditions for the existence of fuzzy state feedback gain and fuzzy observer gain are derived through the numerical solution of a set of coupled linear matrix inequalities. An illustrative example based on the CSTR model is given to design a fuzzy controller.

Neuro-fuzzy rule generation: survey in soft computing framework

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.

Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems

Abstract: FUZZY SET THEORY Classical Set Theory Fuzzy Set Theory Interval Arithmetic Operations on Fuzzy Sets FUZZY LOGIC THEORY Classical Logic Theory The Boolean Algebra Multi-Valued Logic Fuzzy Logic and Approximate Reasoning Fuzzy Relations Fuzzy Logic Rule Base FUZZY SYSTEM MODELING Modeling of the Static Fuzzy Systems Stability Analysis of Discrete-Time Dynamic Fuzzy Systems Modeling of Continuous-Time Dynamic Fuzzy Systems Stability Analysis of Continuous-Time Fuzzy Systems Controllability Analysis of Continuous-Time Dynamic Fuzzy Systems Analysis of Nonlinear Continuous-Time Dynamic Fuzzy Systems FUZZY CONTROL SYSTEMS Classical Programmable Logic Control Fuzzy Logic Control I: A General Model-Free Approach Fuzzy Logic Control II: A General Model-Based Approach FUZZY PID CONTROLLERS Conventional PID Controllers Fuzzy PID Controllers Fuzzy PID Controllers: Stability Analysis ADAPTIVE FUZZY CONTROL Fundamental Adaptive Fuzzy Control Concept Gain Scheduling Fuzzy Self-Tuning Regulator Model Reference Adaptive Fuzzy Systems Dual Control Sub-Optimal Fuzzy Control APPLICATIONS IN FUZZY CONTROL Health Monitoring Fuzzy Diagnostic Systems Fuzzy Control of Image sharpness for Auto-focus Cameras Fuzzy Control for Servo Mechanic Systems Fuzzy PID Controllers for Servo Mechanic Systems Fuzzy Controllers for Robotic Manipulator Note: Each chapter also contains Problems and References

Fuzzy Classifier Design

Abstract: This book about fuzzy classifier design briefly introduces the fundamentals of supervised pattern recognition and fuzzy set theory. Fuzzy if-then classifiers are defined and some theoretical properties thereof are studied. Popular training algorithms are detailed. Non if-then fuzzy classifiers include relational, k-nearest neighbor, prototype-based designs, etc. A chapter on multiple classifier combination discusses fuzzy and non-fuzzy models for fusion and selection.

Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement

Abstract: Fuzzy modeling of high-dimensional systems is a challenging topic. This paper proposes an effective approach to data-based fuzzy modeling of high-dimensional systems. An initial fuzzy rule system is generated based on the conclusion that optimal fuzzy rules cover extrema. Redundant rules are removed based on a fuzzy similarity measure. Then, the structure and parameters of the fuzzy system are optimized using a genetic algorithm and the gradient method. During optimization, rules that have a very low firing strength are deleted. Finally, interpretability of the fuzzy system is improved by fine training the fuzzy rules with regularization. The resulting fuzzy system generated by this method has the following distinct features: (1) the fuzzy system is quite simplified; (2) the fuzzy system is interpretable; and (3) the dependencies between the inputs and the output are clearly shown. This method has successfully been applied to a system that has 11 inputs and one output with 20 000 training data and 80 000 test data.

Dynamic fuzzy neural networks-a novel approach to function approximation

Abstract: In this paper, an architecture of dynamic fuzzy neural networks (D-FNN) implementing Takagi-Sugeno-Kang (TSK) fuzzy systems based on extended radial basis function (RBF) neural networks is proposed. A novel learning algorithm based on D-FNN is also presented. The salient characteristics of the algorithm are: 1) hierarchical on-line self-organizing learning is used; 2) neurons can be recruited or deleted dynamically according to their significance to the system's performance; and 3) fast learning speed can be achieved. Simulation studies and comprehensive comparisons with some other learning algorithms demonstrate that a more compact structure with higher performance can be achieved by the proposed approach.

Fuzzy EMG classification for prosthesis control

Abstract: Proposes a fuzzy approach to classify single-site electromyograph (EMG) signals for multifunctional prosthesis control. While the classification problem is the focus of this paper, the ultimate goal is to improve myoelectric system control performance, and classification is an essential step in the control. Time segmented features are fed to a fuzzy system for training and classification. In order to obtain acceptable training speed and realistic fuzzy system structure, these features are clustered without supervision using the Basic Isodata algorithm at the beginning of the training phase, and the clustering results are used in initializing the fuzzy system parameters. Afterwards, fuzzy rules in the system are trained with the back-propagation algorithm. The fuzzy approach was compared with an artificial neural network (ANN) method on four subjects, and very similar classification results were obtained. It is superior to the latter in at least three points: slightly higher recognition rate; insensitivity to overtraining; and consistent outputs demonstrating higher reliability. Some potential advantages of the fuzzy approach over the ANN approach are also discussed.

General fuzzy min-max neural network for clustering and classification

Abstract: This paper describes a general fuzzy min-max (GFMM) neural network which is a generalization and extension of the fuzzy min-max clustering and classification algorithms of Simpson (1992, 1993). The GFMM method combines supervised and unsupervised learning in a single training algorithm. The fusion of clustering and classification resulted in an algorithm that can be used as pure clustering, pure classification, or hybrid clustering classification. It exhibits a property of finding decision boundaries between classes while clustering patterns that cannot be said to belong to any of existing classes. Similarly to the original algorithms, the hyperbox fuzzy sets are used as a representation of clusters and classes. Learning is usually completed in a few passes and consists of placing and adjusting the hyperboxes in the pattern space; this is an expansion-contraction process. The classification results can be crisp or fuzzy. New data can be included without the need for retraining. While retaining all the interesting features of the original algorithms, a number of modifications to their definition have been made in order to accommodate fuzzy input patterns in the form of lower and upper bounds, combine the supervised and unsupervised learning, and improve the effectiveness of operations. A detailed account of the GFMM neural network, its comparison with the Simpson's fuzzy min-max neural networks, a set of examples, and an application to the leakage detection and identification in water distribution systems are given.

Fuzzy descriptor systems and nonlinear model following control

Abstract: This paper defines a fuzzy descriptor system by extending the ordinary Takagi-Sugeno (T-S) fuzzy model. Several kinds of stability conditions for the fuzzy descriptor system are derived and represented in terms of linear matrix inequalities (LMIs). We illustrate an example of defining the fuzzy descriptor system instead of the ordinary T-S fuzzy model. An LMI design approach is employed to find stable feedback gains and a common Lyapunov function. In addition, this paper presents a nonlinear model following control for the fuzzy descriptor system. A new parallel distributed compensation, the so-called "twin parallel distributed compensations" (TPDC) to realize the nonlinear model following control, is proposed. We present a design technique based on the TPDC. The proposed method is a unified approach to nonlinear model following control. It contains the regulation and servo control problems as special cases.

Classification of Hydrometeors Based on Polarimetric Radar Measurements: Development of Fuzzy Logic and Neuro-Fuzzy Systems, and In Situ Verification

Abstract: A fuzzy logic and neuro-fuzzy system for classification of hydrometeor type based on polarimetric radar measurements is described in this paper. The hydrometeor classification system is implemented by using fuzzy logic and a neural network, where the fuzzy logic is used to infer hydrometeor type, and the neural network learning algorithm is used for automatic adjustment of the parameters of the fuzzy sets in the fuzzy logic system according to prior knowledge. Five radar measurements, namely, horizontal reflectivity ( ZH), differential reflectivity (ZDR), differential propagation phase shift ( KDP), correlation coefficient [rHV(0)], and linear depolarization ratio (LDR), and corresponding altitude, have been used as input variables to the neuro-fuzzy network. The output of the neuro-fuzzy system is one of the many possible hydrometeor types: 1) drizzle, 2) rain, 3) dry and low density snow, 4) dry and high-density crystals, 5) wet and melting snow, 6) dry graupel, 7) wet graupel, 8) small hail, 9) large hail, and 10) a mixture of rain and hail. The neuro-fuzzy classifier is more advantageous than a simple neural network or a fuzzy logic classifier because it is more transparent (instead of a ‘‘black box’’) and can learn the parameter of the system from the past data (unlike a fuzzy logic system). The hydrometeor classifier has been applied to several case studies and the results are compared against in situ observations.

Fuzzy Control and Modeling: Analytical Foundations and Applications

Abstract: From the Publisher: "The emerging, powerful fuzzy control paradigm has led to the worldwide success of countless commercial products and real-world applications. Fuzzy control is exceptionally practical and cost-effective due to its unique ability to accomplish tasks without knowing the mathematical model of the system, even if it is nonlinear, time varying and complex. Nevertheless, compared with the conventional control technology, most fuzzy control applications are developed in an ad hoc manner with little analytical understanding and without rigorous system analysis and design.Fuzzy Control and Modeling is the only book that establishes the analytical foundations for fuzzy control and modeling in relation to the conventional linear and nonlinear theories of control and systems. The coverage is up-to-date, comprehensive, in-depth and rigorous. Numeric examples and applications illustrate the utility of the theoretical development. In the forward to Fuzzy Control and Modeling, Professor Lotfi Zadeh, the founder of fuzzy logic, declares:?Professor Ying?s book contains much that is new, important and detailed? . His linkage of basic theory to real-world applications is very impressive? . The last chapter in the book deals with a subject in which Professor Ying is a foremost authority, namely, application of fuzzy control to biomedical systems?. Professor Ying?s work should go a long way toward countering the view that fuzzy control is a collection of applications without a solid theory. The deep theory of fuzzy control developed by Professor Ying is of great importance both as a theory and as a foundation for major advances in applications of fuzzy control in industry, biomedicine, and otherfields.?Important topics discussed include: *Structures of fuzzy controllers/models with respect to conventional fuzzy controllers/models* Analysis of fuzzy control and modeling in relation to their classical counterparts*Stability analysis of fuzzy systems and design of fuzzy control systems*Sufficient and necessary conditions on fuzzy systems as universal approximators*Real-time fuzzy control systems for treatment of life-critical problems in biomedicineFuzzy Control and Modeling is a self-contained, invaluable resource for professionals and students in diverse technical fields who aspire to analytically study fuzzy control and modeling.About the AuthorHao Ying left the faculty of University of Texas Medical Branch in 2000, and is currently an associate professor in the Department of Electrical Engineering at Wayne State University. He began fuzzy control research in 1981. In 1987, Dr. Ying established the world?s first analytical connection between a fuzzy controller and a conventional controller. In 1989, he developed the world?s first clinical fuzzy control application ? real-time control of blood pressure. Dr. Ying has been making systematic contributions to analytical issues fundamental to fuzzy control and systems ever since."Sponsored by:IEEE Engineering in Medicine and Biology Society.

Fuzzy and Neuro-Fuzzy Intelligent Systems

Abstract: Classical Sets and Fuzzy Sets. Basic Definitions and Terminology: Classical Sets. Fuzzy Sets. Operations on Fuzzy Sets. Classification of t-Norms and t-Conorms. De Morgan Triple and Other Properties of t- and s-Norms. Parameterized t-, s-Norms and Negations. Fuzzy Relations. Cylindrical Extension and Projection of Fuzzy Sets. Extension Principle. Linguistic Variable. Summary.- Approximate Reasoning: Interpretation of Fuzzy Conditional Statement. An Approach to Axiomatic Definition of Fuzzy Implication. Compositional Rule of Inference. Fuzzy Reasoning. Canonical Fuzzy If-Then Rule. Aggregation Operation. Approximate Reasoning Using a Fuzzy Rule Base. Approximate Reasoning with Singletons. Fuzzifiers and Defuzzifiers. Equivalence of Approximate Reasoning Results Using Different Interpretations of If-Then Rules. Numerical Results. Summary.- Artificial Neural Networks: Introduction. Artificial Neural Networks Topologies. Learning in Artificial Neural Networks. Back-Propagation Learning Rule. Modifications of the Classic Back-Propagation Method. Optimization Methods in Neural Networks Learning. Networks with Output Linearly Depending on Parameters. Global Optimization Methods. Summary.- Unsupervised Learning. Clustering Methods: Introduction. Self-Organizing Feature Map. Vector Quantization and Learning Vector Quantization. An Overview of Clustering Methods. Fuzzy Clustering Methods. A Possibilistic Approach to Clustering. A New Generalized Weighted Conditional Fuzzy c-Means. Fuzzy Learning Vector Quantization. Cluster Validity. Summary.- Fuzzy Systems: Introduction. The Mamdani Fuzzy Systems. The Tagaki-Sugeno-Kang Fuzzy Systems. Fuzzy Systems with Parametrized Consequents. Summary.- Neuro-Fuzzy Systems: Introduction. Artificial Neural Network Based Fuzzy Inference Systems. Classifier Based On Neuro-Fuzzy System. ANNBFIS Optimization Using Deterministic Annealing. Further Investigations of Neuro-Fuzzy Systems. Summary.- Applications of Artificial Neural Network Based Fuzzy Inference System: Introduction. Application to Chaotic Time Series Prediction. Application to ECG Signal Compression. Application to Ripley's Synthetic Two-Class Data Classification. Application to the Recognition of Diabetes in Pima Indians. Application to the Iris Problem. Application to Monk's Problems. Application to System Identification. Application to Control. Application to Channel Equalization. Summary.

Fuzzy network profiling for intrusion detection

Abstract: The Fuzzy Intrusion Recognition Engine (FIRE) is an anomaly-based intrusion detection system that uses fuzzy logic to assess whether malicious activity is taking place on a network. It uses simple data mining techniques to process the network input data and help expose metrics that are particularly significant to anomaly detection. These metrics are then evaluated as fuzzy sets. FIRE uses a fuzzy analysis engine to evaluate the fuzzy inputs and trigger alert levels for the security administrator. This paper describes the components in the FIRE architecture and explains their roles. Particular attention is given to explaining the benefits of data mining and how this can improve the meaningfulness of the fuzzy sets. Fuzzy rules are developed for some common intrusion detection scenarios. The results of tests with actual network data and actual malicious attacks are described. The FIRE IDS can detect a wide-range of common attack types.

Genetic reinforcement learning through symbiotic evolution for fuzzy controller design

Abstract: An efficient genetic reinforcement learning algorithm for designing fuzzy controllers is proposed in this paper. The genetic algorithm (GA) adopted in this paper is based upon symbiotic evolution which, when applied to fuzzy controller design, complements the local mapping property of a fuzzy rule. Using this Symbiotic-Evolution-based Fuzzy Controller (SEFC) design method, the number of control trials, as well as consumed CPU time, are considerably reduced when compared to traditional GA-based fuzzy controller design methods and other types of genetic reinforcement learning schemes. Moreover, unlike traditional fuzzy controllers, which partition the input space into a grid, SEFC partitions the input space in a flexible way, thus creating fewer fuzzy rules. In SEFC, different types of fuzzy rules whose consequent parts are singletons, fuzzy sets, or linear equations (TSK-type fuzzy rules) are allowed. Further, the free parameters (e.g., centers and widths of membership functions) and fuzzy rules are all tuned automatically. For the TSK-type fuzzy rule especially, which put the proposed learning algorithm in use, only the significant input variables are selected to participate in the consequent of a rule. The proposed SEFC design method has been applied to different simulated control problems, including the cart-pole balancing system, a magnetic levitation system, and a water bath temperature control system. The proposed SEFC has been verified to be efficient and superior from these control problems, and from comparisons with some traditional GA-based fuzzy systems.

Fuzzy Cognitive Maps in modeling supervisory control systems

Abstract: This paper investigates a hybrid methodology that combines fuzzy logic and neural networks, Fuzzy Cognitive Map (FCM), for modeling and controlling Supervisory Control Systems. A mathematical description of Fuzzy Cognitive Maps (FCM) will be presented and new construction methods will be extensively examined. A Fuzzy Cognitive Map will be developed to model and control a process example and the Supervisor-FCM model characteristics will be discussed. There is a going need for more autonomous and intelligent systems, especially in Complex Systems area, the application of Fuzzy Cognitive Maps for modeling the Supervisor may contribute to develop more sophisticated systems.

A comparative study on sufficient conditions for Takagi-Sugeno fuzzy systems as universal approximators

Abstract: Universal approximation is the basis of theoretical research and practical applications of fuzzy systems. Studies on the universal approximation capability of fuzzy systems have achieved great progress in recent years. In this paper, linear Takagi-Sugeno (TS) fuzzy systems that use linear functions of input variables as rule consequent and their special case, named simplified fuzzy systems that use fuzzy singletons as rule consequent, are investigated. On condition that overlapped fuzzy sets are employed, new sufficient conditions for simplified fuzzy systems and linear TS fuzzy systems as universal approximators are given, respectively. Then, a comparative study on existing sufficient conditions is carried out with numeric examples.

Genetic fuzzy learning

Abstract: A hybrid approach to fuzzy supervised learning is presented. It is based on a genetic-neuro learning algorithm. The mixed-genetic coding adopted involves only the premises of the fuzzy rules. The conclusions are derived through a least-squares solution of an over-determined system using the singular value decomposition (SVD) algorithm. The paper presents the results obtained with C++ software called GEFREX that implements the proposed algorithm. The main characteristic of the algorithm is the compactness of the fuzzy systems extracted. Several comparisons ranging from approximation problems, classification problems, and time series predictions show that GEFREX reaches a smaller error than found in previous works with the same or a smaller number of rules. Further, it succeeds in identifying significant features. Although the SVD is used extensively, the learning time is decidedly reduced in comparison with previous work.

Hybrid neural systems

Abstract: An Overview of Hybrid Neural Systems.- An Overview of Hybrid Neural Systems.- Structured Connectionism and Rule Representation.- Layered Hybrid Connectionist Models for Cognitive Science.- Types and Quantifiers in SHRUTI - A Connectionist Model of Rapid Reasoning and Relational Processing.- A Recursive Neural Network for Reflexive Reasoning.- A Novel Modular Neural Architecture for Rule-Based and Similarity-Based Reasoning.- Addressing Knowledge-Representation Issues in Connectionist Symbolic Rule Encoding for General Inference.- Towards a Hybrid Model of First-Order Theory Refinement.- Distributed Neural Architectures and Language Processing.- Dynamical Recurrent Networks for Sequential Data Processing.- Fuzzy Knowledge and Recurrent Neural Networks: A Dynamical Systems Perspective.- Combining Maps and Distributed Representations for Shift-Reduce Parsing.- Towards Hybrid Neural Learning Internet Agents.- A Connectionist Simulation of the Empirical Acquisition of Grammatical Relations.- Large Patterns Make Great Symbols: An Example of Learning from Example.- Context Vectors: A Step Toward a "Grand Unified Representation".- Integration of Graphical Rules with Adaptive Learning of Structured Information.- Transformation and Explanation.- Lessons from Past, Current Issues, and Future Research Directions in Extracting the Knowledge Embedded in Artificial Neural Networks.- Symbolic Rule Extraction from the DIMLP Neural Network.- Understanding State Space Organization in Recurrent Neural Networks with Iterative Function Systems Dynamics.- Direct Explanations and Knowledge Extraction from a Multilayer Perceptron Network that Performs Low Back Pain Classification.- High Order Eigentensors as Symbolic Rules in Competitive Learning.- Holistic Symbol Processing and the Sequential RAAM: An Evaluation.- Robotics, Vision and Cognitive Approaches.- Life, Mind, and Robots.- Supplementing Neural Reinforcement Learning with Symbolic Methods.- Self-Organizing Maps in Symbol Processing.- Evolution of Symbolisation: Signposts to a Bridge between Connectionist and Symbolic Systems.- A Cellular Neural Associative Array for Symbolic Vision.- Application of Neurosymbolic Integration for Environment Modelling in Mobile Robots.

Constructing fuzzy models with linguistic integrity from numerical data-AFRELI algorithm

Abstract: This paper presents an algorithm to extract rules re- lating input/output data and including prior knowledge. The rules are created in the environment of fuzzy systems. The fuzzy sets describing the system are constructed within a framework of lin- guistic integrity to guarantee its interpretability in the linguistic context. Two algorithms are presented in this paper. The main al- gorithm is the autonomous fuzzy rule extractor with linguistic in- tegrity (AFRELI). This algorithm is complemented with the use of the FuZion algorithm created to merge consecutive member- ship functions, while guaranteeing the distinguish ability between fuzzy sets. Comparisons with other proposed methods show a good tradeoff between accuracy and interpretability.

Extracting Fuzzy Rules from Data for Function Approximation and Pattern Classification

Abstract: Extracting fuzzy rules from data allows relationships in the data to be modeled by "if-then" rules that are easy to understand, verify, and extend. This paper presents methods for extracting fuzzy rules for both function approximation and pattern classification. The rule extraction methods are based on estimating clusters in the data; each cluster obtained corresponds to a fuzzy rule that relates a region in the input space to an output region (or, in the case of pattern classification, to an output class). After the number of rules and initial rule parameters are obtained by cluster estimation, the rule parameters are optimized by gradient descent. Applications to a function approximation problem and to a pattern classification problem are also illustrated.

Unsupervised feature evaluation: a neuro-fuzzy approach

Abstract: Demonstrates a way of formulating neuro-fuzzy approaches for both feature selection and extraction under unsupervised learning. A fuzzy feature evaluation index for a set of features is defined in terms of degree of similarity between two patterns in both the original and transformed feature spaces. A concept of flexible membership function incorporating weighted distance is introduced for computing membership values in the transformed space. Two new layered networks are designed. The tasks of membership computation and minimization of the evaluation index, through unsupervised learning process, are embedded into them without requiring the information on the number of clusters in the feature space. The network for feature selection results in an optimal order of individual importance of the features. The other one extracts a set of optimum transformed features, by projecting n-dimensional original space directly to n'-dimensional (n'

Self-organized fuzzy system generation from training examples

Abstract: In the synthesis of a fuzzy system two steps are generally employed: the identification of a structure and the optimization of the parameters defining it. The paper presents a methodology to automatically perform these two steps in conjunction using a three-phase approach to construct a fuzzy system from numerical data. Phase 1 outlines the membership functions and system rules for a specific structure, starting from a very simple initial topology. Phase 2 decides a new and more suitable topology with the information received from the previous step; it determines for which variable the number of fuzzy sets used to discretize the domain must be increased and where these new fuzzy sets should be located. This, in turn, decides in a dynamic way in which part of the input space the number of fuzzy rules should be increased. Phase 3 selects from the different structures obtained to construct a fuzzy system the one providing the best compromise between the accuracy of the approximation and the complexity of the rule set. The accuracy and complexity of the fuzzy system derived by the proposed self-organized fuzzy rule generation procedure (SOFRG) are studied for the problem of function approximation. Simulation results are compared with other methodologies such as artificial neural networks, neuro-fuzzy systems, and genetic algorithms.

Soft computing in case based reasoning

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.

Scheduling under Fuzziness

Abstract: Fuzzy Knowledge Representation in Scheduling: I.B. Turksen, M.H.F. Zarandi, M. Dudzic: Caster Scheduling System Analysis with Fuzzy Technology.- M. Litoiu, R. Tadei: Dynamic Scheduling on Distributed Real-Time Systems by Self-Learning Fuzzy Algorithms.- Fuzzy Constraints in Scheduling: H. Fagier, C. Thierry: The Use of Possibilistic Decision Theory in Manufacturing, Planning and Control: Recent Results in Fuzzy Master Production Scheduling.- P. Fortemps: Introducing Flexibility in Scheduling: The Preference Approach.- H. Ishii: Scheduling Problems with Fuzzy Constraints.- H. Ishibuchi, T. Murata: Flowshop Scheduling with Fuzzy Duedate and Fuzzy Processing Time.- Fuzzy Uncertainty in Scheduling: G. Adamopoulos, C.P. Pappis, N.I. Karacapilidis: A Methodology for Solving a Range of Scheduling Problems under Uncertainty.- S. Chanas, A. Kasperski, D. Kuchta: Two Approaches to Fuzzy Flow Shop Problem.- M. Hapke, R. Slowinski: Fuzzy Set Approach to Multi-Objective and Multi-Mode Project Scheduling under Uncertainty.- M. Vlach: Single Machine Scheduling under Fuzziness.- L. Geneste, B. Grabot, P. Moutarlier: Scheduling of Heterogeneous Data Using Fuzzy Logic in a Customer-Subcontractor Context.- N. Kubota, T. Fukuda: Virus-Evolutionary Genetic Algorithm for Sequencing Jobs in Fuzzy Environment.- N.I. Karacapilidis, C.P. Pappis, G. Adamopulos: Fuzzy Set Approaches to Lot Sizing.

On multistage fuzzy neural network modeling

Abstract: In the past couple of years, there has been increasing interest in the fusion of neural networks and fuzzy logic. Most of the existing fuzzy neural network (FNN) models have been proposed to implement different types of single-stage fuzzy reasoning mechanisms and inevitably they suffer from the dimensionality problem when dealing with complex real-world problems. To address the problem, FNN modeling based on multistage fuzzy reasoning (MSFR) is pursued here and two hierarchical network models, namely incremental type and aggregated type, are introduced. The new models called multistage FNN (MSFNN) model a hierarchical fuzzy rule set that allows the consequence of a rule passed to another as a fact through the intermediate variables. From the stipulated input-output data pairs, they can generate an appropriate fuzzy rule set through structure and parameter learning procedures proposed in this paper. In addition, we have particularly addressed the input selection problem of these two types of multistage network models and proposed two efficient methods for them. The effectiveness of the proposed MSFNN models in handling high-dimensional problems is demonstrated through various numerical simulations.

Generalization of adaptive neuro-fuzzy inference systems

Abstract: The adaptive network-based fuzzy inference systems (ANFIS) of Jang (1993) is extended to the generalized ANFIS (GANFIS) by proposing a generalized fuzzy model (GFM) and considering a generalized radial basis function (GRBF) network. The GFM encompasses both the Takagi-Sugeno (TS)-model and the compositional rule of inference (CRI) model. The conditions by which the proposed GFM converts to TS-model or the CRI-model are presented. The basis function in GRBF is a generalized Gaussian function of three parameters. The architecture of the GRBF network is devised to learn the parameters of GFM, where the GRBF network and GFM have been proved to be functionally equivalent. It Is shown that GRBF network can be reduced to either the standard RBF or the Hunt's RBF network. The issue of the normalized versus the non-normalized GRBF networks is investigated in the context of GANFIS. An interesting property of symmetry on the error surface of GRBF network is investigated. The proposed GANFIS is applied to the modeling of a multivariable system like stock market.