Generating fuzzy rules by learning from examples

A general method is developed to generate fuzzy rules from numerical data. This new method consists of five steps: Step 1 divides the input and output spaces of the given numerical data into fuzzy regions; Step 2 generates fuzzy rules from the given data; Step 3 assigns a degree of each of the generated rules for the purpose of resolving conflicts among the generated rules; Step 4 creates a combined fuzzy rule base based on both the generated rules and linguistic rules of human experts; and, Step 5 determines a mapping from input space to output space based on the combined fuzzy rule base using a defuzzifying procedure. The mapping is proved to be capable of approximating any real continuous function on a compact set to arbitrary accuracy. Applications to truck backer-upper control and time series prediction problems are presented. For the truck control problem, the performance of this new method is compared with a neural network controller and a pure limited-rule fuzzy controller; the new method shows the best performance. For the time series prediction problem, results are compared by using the new method and a neural network predictor for the Mackey-Glass chaotic time series.