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Editorial: fuzzy models-what are they, and why?
J. E. Bezdek
vol. 1, no. 1, pp. 1-6, Feb. 1993

A brief introduction to the basic ideas of fuzzy sets is given. The evolution of fuzzy models and of the IEEE Transactions on Fuzzy Systems is recounted.

A fuzzy-logic-based approach to qualitative modeling
M. Sugeno, T. Yasukawa
vol. 1, no. 1, pp. 7-31, Feb. 1993

A general approach to qualitative modeling based on fuzzy logic is discussed. The method of qualitative modeling is divided into two parts: fuzzy modeling and linguistic approximation. It is proposed to use a fuzzy clustering method (fuzzy c-means method) to identify the structure of a fuzzy model. To clarify the advantages of the proposed method, some examples of modeling, among them a model of a dynamical process and a model of a human operator's control action, are given.

Fuzzy min-max neural networks. II. Clustering
P. K. Simpson
vol. 1, no. 1, pp. 32-45, Feb. 1993

For Pt. I, see IEEE Transactions on Neural Networks, vol.3, p.776-786 (1992). Some background concerning the development of the fuzzy min-max clustering neural network is provided, and a comparison is made with similar work that has recently emerged. A brief description of fuzzy sets, pattern clustering, and their synergistic combination is presented. The fuzzy min-max clustering neural network is explained in detail, and examples of its clustering performance are given. A description of problems that need to be addressed and a list of some potential applications are given.

Fuzzy control of pH using genetic algorithms
C. L. Karr, E. J. Gentry
vol. 1, no. 1, pp. 46-53, Feb. 1993

Establishing suitable control of pH, a difficult problem because of inherent nonlinearities and frequently changing process dynamics, is addressed. A technique for producing adaptive fuzzy logic controllers (FLCs) that are capable of effectively managing such systems is applied. A genetic algorithm (GA) alters the membership functions employed by a conventional FLC. GAs are search algorithms based on the mechanics of natural genetics that are able to rapidly locate near-optimal solutions to difficult problems. The technique is used to produce an adaptive GA-FLC for a laboratory acid-base experiment. Nonlinearities in the laboratory system are associated with the logarithmic pH scale, and changing process dynamics are introduced by altering system parameters such as the desired set point and the concentration and buffering capacity of input solutions. Results indicate that FLCs augmented with GAs offer a powerful alternative to conventional process control techniques.

Self-organization for object extraction using a multilayer neural network and fuzziness measures
A. Ghosh, N. R. Pal, S. K. Pal
vol. 1, no. 1, pp. 54-68, Feb. 1993

The feedforward multilayer perceptron (MLP) with back-propagation of error is described. Because the use of this network requires a set of labeled input-output, it cannot be used for segmentation of images when only one image is available. A self-organizing multilayer neural network architecture suitable for image processing is proposed. The proposed architecture is also a feedforward one with backpropagation of errors, but like MLP it does not require any supervised learning. Each neuron is connected to the corresponding neuron in the previous layer and the set of neighbors of that neuron. The output status of neurons in the output layer is described as a fuzzy set. A fuzziness measure of this fuzzy set is used as a measured of error in the system (instability of the network). Learning rates for various measures of fuzziness have been theoretically and experimentally studied. An application of the proposed network in object extraction from noisy scenes is also demonstrated.

SLIDE: a simple adaptive defuzzification method
R. R. Yager, D. P. Filev
vol. 1, no. 1, pp. 69-78, Feb. 1993

A parameterized family of defuzzification operators called the semilinear defuzzification (SLIDE) method is introduced. This method is based on a simple transformation of the fuzzy output set of the controller. An algorithm for learning the parameter from a data set is suggested. To simplify the parameter learning, a modified version of the SLIDE method which results in a simple learning algorithm is proposed. The development of the learning algorithm is based on the use of the Kalman filter.