METHODS OF FUZZY PATTERN RECOGNITION.pdf

SCIENTIFIC PROCEEDINGS OF RIGA TECHNICAL UNIVERSITY Computer Science Information Technology and Management Science _____________________________________________________________________________________2002 METHODS OF FUZZY PATTERN RECOGNITION R. Grekovs Keywords: pattern recognition, fuzzy sets, composition of fuzzy relations, fuzzy rules, fuzzy clustering 1. Introduction. Technique of learning fuzzy rules The first approaches to learning fuzzy rules from data were based on neural network concepts (perceptron-based methods) [1]. The underlying principal of all these models is that they need an evaluation of the output that the fuzzy rules represented by the neural network produce for a given input. This can be an error measure or simply a binary evaluation. Then the models aim is at improving the output by changing the fuzzy sets appearing in the rules in a suitable way. This change is usually derived by some back propagation technique or simply by heuristic algorithm. They are usually designed for the adaptation of the fuzzy sets, but sometimes also involve a simple strategy for choosing an initial set of rules, adding or deleting rules while learning. They strongly rely on the simple architecture that allows identify how changes in the fuzzy sets effect the output. Therefore, they are quite suited for tuning the fuzzy sets in this type of rules, but are not designed for learning rules. Another popular approach to learning fuzzy rules is based on evolutionary algorithms. Evolutionary algorithms are a quite general technique for parameter optimisation that is motivated by concepts of biological evolution. It starts with an initial population of possible solutions of the optimisation problem that is usually randomly generated. Then changes some of the values of the population (mutation) and apply biological operators like crossover that exchanges values between different solutions. Then the best solutions are selected