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...