Fuzzy Logic and Expert Systems Applications (eBook)

Cornelius T. Leondes (Autor)

eBook Download: PDF

1998 | 1. Auflage
416 Seiten
Elsevier Science (Verlag)
978-0-08-055319-1 (ISBN)

Infos

This volume covers the integration of fuzzy logic and expert systems. A vital resource in the field, it includes techniques for applying fuzzy systems to neural networks for modeling and control, systematic design procedures for realizing fuzzy neural systems, techniques for the design of rule-based expert systems using the massively parallel processing capabilities of neural networks, the transformation of neural systems into rule-based expert systems, the characteristics and relative merits of integrating fuzzy sets, neural networks, genetic algorithms, and rough sets, and applications to system identification and control as well as nonparametric, nonlinear estimation. Practitioners, researchers, and students in industrial, manufacturing, electrical, and mechanical engineering, as well as computer scientists and engineers will appreciate this reference source to diverse application methodologies.

Key Features
* Fuzzy system techniques applied to neural networks for modeling and control
* Systematic design procedures for realizing fuzzy neural systems
* Techniques for the design of rule-based expert systems
* Characteristics and relative merits of integrating fuzzy sets, neural networks, genetic algorithms, and rough sets
* System identification and control
* Nonparametric, nonlinear estimation
Practitioners, researchers, and students in industrial, manufacturing, electrical, and mechanical engineering, as well as computer scientists and engineers will find this volume a unique and comprehensive reference to these diverse application methodologies

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This volume covers the integration of fuzzy logic and expert systems. A vital resource in the field, it includes techniques for applying fuzzy systems to neural networks for modeling and control, systematic design procedures for realizing fuzzy neural systems, techniques for the design of rule-based expert systems using the massively parallel processing capabilities of neural networks, the transformation of neural systems into rule-based expert systems, the characteristics and relative merits of integrating fuzzy sets, neural networks, genetic algorithms, and rough sets, and applications to system identification and control as well as nonparametric, nonlinear estimation. Practitioners, researchers, and students in industrial, manufacturing, electrical, and mechanical engineering, as well as computer scientists and engineers will appreciate this reference source to diverse application methodologies. - Fuzzy system techniques applied to neural networks for modeling and control- Systematic design procedures for realizing fuzzy neural systems- Techniques for the design of rule-based expert systems- Characteristics and relative merits of integrating fuzzy sets, neural networks, genetic algorithms, and rough sets- System identification and control- Nonparametric, nonlinear estimation Practitioners, researchers, and students in industrial, manufacturing, electrical, and mechanical engineering, as well as computer scientists and engineers will find this volume a unique and comprehensive reference to these diverse application methodologies

Inhalt

Front Cover 1
Fuzzy Logic and Expert Systems Applications 4
Copyright Page 5
Contents 6
Contributors 14
Preface 16
Chapter 1. Fuzzy Neural Networks Techniques and Their Applications 22
I. Introduction 22
II. Fuzzy Classification and Fuzzy Modeling by Nonfuzzy Neural Networks 27
III. Interval-Arithmetic-Based Neural Networks 48
IV. Fuzzified Neural Networks 61
V. Conclusion 72
References 73
Chapter 2. Implementation of Fuzzy Systems 78
I. Introduction 78
II. Structure of Fuzzy Systems for Modeling and Control 81
III. Design 1: A Fuzzy Neural Network with an Additional OR Layer 97
IV. Design 2: A Fuzzy Neural Network Based on Hierarchical Space Partitioning 115
V. Conclusion 138
Appendix 139
References 141
Chapter 3. Neural Networks and Rule-Based Systems 144
I. Introduction 144
II. Nonlinear Thresholded Artificial Neurons 145
III. Production Rules 146
IV. Forward Chaining 148
V. Chunking 153
VI. Neural Tools for Uncertain Reasoning: Toward Hybrid Extensions 161
VII. Qualitative and Quantitative Uncertain Reasoning 166
VIII. Purely Neural, Rule-Based Diagnostic System 179
IX. Conclusions 192
References 194
Chapter 4. Construction of Rule-Based Intelligent Systems 196
I. Introduction 196
II. Representation of a Neuron 197
III. Converting Neural Networks to Boolean Functions 200
IV. Example Application of Boolean Rule Extraction 206
V. Network Design, Pruning, and Weight Decay 208
VI. Simplifying the Derived Rule Base 213
VII. Example of the Construction of a Rule-Based Intelligent System 218
VIII. Using Rule Extraction to Verify the Networks 223
IX. Conclusions 229
References 230
Chapter 5. Expert Systems in Soft Computing Paradigm 232
I. Introduction 232
II. Expert Systems: Some Problems and Relevance of Soft Computing 235
III. Connectionist Expert Systems: A Review 246
IV. Neuro-Fuzzy Expert Systems 248
V. Other Hybrid Models 255
VI. Conclusions 258
References 258
Chapter 6. Mean-Value-Based Functional Reasoning Techniques in the Development of Fuzzy-Neural Network Control Systems 264
I. Introduction 264
II. Fuzzy Reasoning Schemes 266
III. Design of the Conclusion Part in Functional Reasoning 269
IV. Fuzzy Gaussian Neural Networks 270
V. Attitude Control Application Example 279
VI. Mobile Robot Example 291
VII. Conclusions 302
References 303
Chapter 7. Fuzzy Neural Network Systems in Model Reference Control Systems 306
I. Introduction 306
II. Fuzzy Neural Network 307
III. Mapping Capability of the Fuzzy Neural Network 320
IV. Model Reference Control System Using a Fuzzy Neural Network 326
V. Simulation Results 330
VI. Conclusions 333
References 333
Chapter 8. Wavelets in Identification 336
I. Introduction, Motivations, Basic Problems 336
II "Classical" Methods of Nonlinear System Identification 348
III. Wavelets: What They Are, and Their Use in Approximating Functions 365
IV. Wavelets: Their Use in Nonparametric Estimation 377
V. Wavelet Network for Practical System Identification 384
VI. Fuzzy Models: Expressing Prior Knowledge in Nonlinear Nonparametric Models 391
VII. Experimental Results 400
VIII. Discussion and Conclusions 418
IX. Appendix: Three Methods for Regressor Selection 421
References 430
Index 434

Textauszug

Preface

Cornelius T. Leondes

Inspired by the structure of the human brain, artificial neural networks have been widely applied to fields such as pattern recognition, optimization, coding, control, etc., because of their ability to solve cumbersome or intractable problems by learning directly from data. An artificial neural network usually consists of a large number of simple processing units, i.e., neurons, via mutual interconnection. It learns to solve problems by adequately adjusting the strength of the interconnections according to input data. Moreover, the neural network adapts easily to new environments by learning, and can deal with information that is noisy, inconsistent, vague, or probabilistic. These features have motivated extensive research and developments in artificial neural networks. This volume is probably the first rather comprehensive treatment devoted to the broad areas of algorithms and architectures for the realization of neural network systems. Techniques and diverse methods in numerous areas of this broad subject are presented. In addition, various major neural network structures for achieving effective systems are presented and illustrated by examples in all cases. Numerous other techniques and subjects related to this broadly significant area are treated.

The remarkable breadth and depth of the advances in neural network systems with their many substantive applications, both realized and yet to be realized, make it quite evident that adequate treatment of this broad area requires a number of distinctly titled but well-integrated volumes. This is the sixth of seven volumes on the subject of neural network systems and it is entitled Fuzzy Logic and Expert Systems Applications. The entire set of seven volumes contains

Volume 1: Algorithms and Architectures

Volume 2: Optimization Techniques

Volume 3: Implementation Techniques

Volume 4: Industrial and Manufacturing Systems

Volume 5: Image Processing and Pattern Recognition

Volume 6: Fuzzy Logic and Expert Systems Applications

Volume 7: Control and Dynamic Systems

The first contribution to this volume is “Fuzzy Neural Networks Techniques and Their Applications,” by Hisao Ishibuchi and Manabu Nii. Fuzzy logic and neural networks have been combined in various ways. In general, hybrid systems of fuzzy logic and neural networks are often referred to as fuzzy neural networks, which in turn can be classified into several categories. The following list is one example of such a classification of fuzzy neural networks:

1. Fuzzy rule-based systems with learning ability,

2. Fuzzy rule-based systems represented by network architectures,

3. Neural networks for fuzzy reasoning,

4. Fuzzified neural networks,

5. Other approaches.

The classification of a particular fuzzy neural network into one of these five categories is not always easy, and there may be different viewpoints for classifying neural networks. This contribution focuses on fuzzy classification and fuzzy modeling. Nonfuzzy neural networks and fuzzified neural networks are used for these tasks. In this contribution, fuzzy modeling means modeling with nonlinear fuzzy number valued functions. Included in this contribution is a description of how feedforward neural networks can be extended to handle the fuzziness of training data. The many implications of this are then treated sequentially and in detail. A rather comprehensive set of illustrative examples is included which clearly manifest the significant effectiveness of fuzzy neural network systems in a variety of applications.

The next contribution is “Implementation of Fuzzy Systems,” by Chu Kwong Chak, Gang Feng, and Marimuthu Palaniswami. The expanding popularity of fuzzy systems appears to be related to its ability to deal with complex systems using a linguistic approach. Although many applications have appeared in systems science, especially in modeling and control, there is no systematic procedure for fuzzy system design. The conventional approach to design is to capture a set of linguistic fuzzy rules given by human experts. This empirical design approach encounters a number of problems, i.e., that the design of optimal fuzzy systems is very difficult because no systematic approach is available, that the performance of the fuzzy systems can be inconsistent because the fuzzy systems depend mainly on the intuitiveness of individual human expert, and that the resultant fuzzy systems lack adaptation capability. Training fuzzy systems by using a set of input–output data captured from the complex systems, via some learning algorithms, is known to generate or modify the linguistic fuzzy rules. A neural network is a suitable tool for achieving this purpose because of its capability for learning from data. This contribution presents an in-depth treatment of the neural network implementation of fuzzy systems for modeling and control. With the new space partitioning techniques and the new structure of fuzzy systems developed in this contribution, radial basis function neural networks and sigmoid function neural networks are successfully applied to implement higher order fuzzy systems that effectively treat the problem of rule explosion. Two new fuzzy neural networks along with learning algorithms, such as the Kalman filter algorithm and some hybrid learning algorithms, are presented in this contribution. These fuzzy neural networks can achieve self-organization and adaptation and hence improve the intelligence of fuzzy systems. Some simulation examples are shown to support the effectiveness of the fuzzy neural network approach. An array of illustrative examples clearly manifests the substantive effectiveness of fuzzy neural network system techniques.

The next contribution is “Neural Networks and Rule-Based Systems,” by Aldo Aiello, Ernesto Burattini, and Guglielmo Tamburrini. This contribution presents methods of implementing a wide variety of effective rule-based reasoning processes by means of networks formed by nonlinear thresholded neural units. In particular, the following networks are examined:

1. Networks that represent knowledge bases formed by propositional production rules and that perform forward chaining on them.

2. A network that monitors the elaboration of the forward chaining system and learns new production rules by an elementary chunking process.

3. Networks that perform qualitative forms of uncertain reasoning, such as hypothetical reasoning in two-level casual networks and the application of preconditions in default reasoning.

4. Networks that simulate elementary forms of quantitative uncertain reasoning.

The utilization of these techniques is exemplified by the overall structure and implementation features of a purely neural, rule-based expert system for a diagnostic task and, as a result, their substantive effectiveness is clearly manifested.

The next contribution is “Construction of Rule-Based Intelligent Systems,” by Graham P. Fletcher and Chris J. Hinde. It is relatively straightforward to transform a propositional rule-based system into a neural network. However, the transformation in the other direction has proved a much harder problem to solve. This contribution explains techniques that allow neurons, and thus networks, to be expressed as a set of rules. These rules can then be used within a rule-based system, turning the neural network into an important tool in the construction of rule-based intelligent systems. The rules that have been extracted, as well as forming a rule-based implementation of the network, have further important uses. They also represent information about the internal structures that build up the hypothesis and, as such, can form the basis of a verification system. This contribution also considers how the rules can be used for this purpose. Various illustrative examples are included.

The next contribution is “Expert Systems in Soft Computing Paradigm,” by Sankar K. Pal and Sushmita Mitra. This contribution is a rather comprehensive treatment of the soft computing paradigm, which is the integration of different computing paradigms such as fuzzy set theory, neural networks, genetic algorithms, and rough set theory. The intent of the soft computing paradigm is to generate more efficient hybrid systems. The purpose of soft computing is to provide flexible information processing capability for handling real life ambiguous situations by exploiting the tolerance for imprecision, uncertainty, approximate reasoning, and partial truth to achieve tractability, robustness, and low cost. The guiding principle is to devise methods of computation which lead to an acceptable solution at low cost by seeking an approximate solution to an imprecisely/precisely formulated problem. Several illustrative examples are included.

The next contribution is “Mean-Value-Based Functional Reasoning Techniques in the Development of Fuzzy-Neural Network Control Systems,” by Keigo Watanabe and Spyros G. Tzafestas. This contribution reviews first conventional functional reasoning, simplified reasoning, and mean-value-based functional reasoning methods. Design techniques which utilize these fuzzy reasoning methods based on...

Produkt-Details

• Erscheint lt. Verlag: 9.2.1998
• Sprache: englisch
• Themenwelt: Informatik ► Grafik / Design ► Digitale Bildverarbeitung
• Themenwelt: Mathematik / Informatik ► Informatik ► Netzwerke
• Themenwelt: Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge
• Themenwelt: Mathematik / Informatik ► Informatik ► Software Entwicklung
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Angewandte Mathematik
• Themenwelt: Technik ► Elektrotechnik / Energietechnik
• Themenwelt: Technik ► Nachrichtentechnik
• ISBN-10: 0-08-055319-2 / 0080553192
• ISBN-13: 978-0-08-055319-1 / 9780080553191