E L S E V I E R Fuzzy Sets and Systems 79 (1996) 1-2

f U | Z Y sets and systems

Preface

The idea for this special issue developed as a re- sult of a colloquium on Neural Networks and Fuzzy Logic in Measurement and Control which took place in Liverpool in March 1993. Several workers in the field suggested that since the combi- nation of fuzzy systems with neural networks was a development which was increasingly being con- sidered it would be opportune to have an issue of the journal devoted to this topic.

Rather than simply selecting and reproducing papers from the colloquium we sought to present a broader picture of the way in which the subject was developing by asking certain authors from the colloquium to tailor their original submissions and also by putting out a general call for papers. The result is a selection which can conveniently be split into three parts:

(1) a section which presents examples of fuzzy systems applied to modelling and control;

(2) two papers dealing with hybrid structures, one of which, included to indicate yet further devel- opments, shows how genetic algorithms can be combined with fuzzy networks, while the other acts as an introduction to the final part of the journal and discusses in depth some of the key features and problems of the neuro-fuzzy approach;

(3) a final section which begins with a pure neu- ral-network paper and is followed by two papers which demonstrate different approaches to neuro- fuzzy systems.

The first paper describes a fuzzy model-based controller which utilises a conventional predictive controller design in conjunction with a fuzzy rela- tional model. The controller is described and com- pared with existing fuzzy controllers. The second paper presents a model reference approach to ad-

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just the rule-base for an adaptive controller for a subsea vehicle. The next paper describes how fuzzy set theory is combined with an expert system approach to enable choices to be made between a number of control strategies running in parallel. These choices are performed in the environment of a practical implementation rather than by simula- tions. An interesting medical application using hierarchical modelling completes this first section. It uses two self-organising fuzzy modelling config- urations: (i) an on-line approach to generate rules from input and output data and (ii) the use of off-line data when no meaningful direct measure- ment of system output can be obtained.

The central section consists of two papers. The first introduces a novel fuzzy controller which has similarities to radial basis functions in neural net- works. The controller is trained by means of a gen- etic algorithm which uses a real-valued parameter encoding scheme. This paper is an example of an increasing trend in which approaches from several strands of computation intelligence are coming to- gether and are creating their own synergy. The next paper acts as a pivot for the whole of the special issue. It compares and contrasts apparently differ- ent approaches for representing linguistic fuzzy algorithms as well as discussing their relevance to neuro-fuzzy adaptive modelling and control schemes. Discrete versus continuous implementa- tions are discussed. The paper sets out a consistent approach to the implementation of neuro-fuzzy al- gorithms and attempts to relate it to more conven- tional systems.

The final section begins with a paper which uses a pure neural network approach based on the multi-layer perceptron network. The mechanisms

2 Pre[ktce / Fuzz): Sets and Systems 79 (1996) l 2

of this non-linear modelling technique are ex- plained and such aspects as model-order, over- parameterisation and choice of excitation signals are discussed. These ideas are then illustrated by their application to two different processes. Having looked at an example of the pure neural network approach, there follow two quite different ap- proaches to creating neuro-fuzzy paradigms. In the first, a sliding Gaussian pattern of excitations is applied across several input nodes of a conven- tional multi-layer perceptron network. This spread- encoding exhibits similarities to conventional fuzzy logic inputs and is the most direct approach to the implementation of a neuro-fuzzy network. The last paper considers the partitioning of a process into several fuzzy operating regions with a global out- put being obtained by centre of gravity defuzzifica-

tion. In addition, by adding an extra fuzzification layer to a conventional feedforward network a fault diagnosis network has been produced.

The guest editors would like to thank all the authors for their contributions to this special issue and the referees for their painstaking work. We would also like to express a special thanks to Professor Derek Linkens for his guidance and to Professor H.J. Zimmermann for his valuable support and patience.

George Page Barry Gomm John Moores University Liverpool June 1995