Generating Optimal Fuzzy If-Then Rules Using the Partition of Fuzzy Input Space

This paper proposes an extended fuzzy entropy-based-method for selecting an optimal number of fuzzy rules to construct a compact fuzzy classification system with high classification power. An optimal number of rules are generated through the optimal partition of input space via the extended fuzzy entropy to define an index of feature evaluation in pattern recognition problems decreases as the reliability of a feature in characterizing and discriminating different classes increases. A set of fuzzy if-then rules is coded into a string and treated as an individual in genetic algorithms. The fitness of each individual is specified by the two objectives. The performance of the proposed method for training data and test data is examined by computer simulations on the Mackey-Glass chaotic time series prediction.