Evolving rule-based models: a tool for intelligent adaptation

An online approach for rule-base evolution by recursive adaptation of rule structure and parameters is described . An integral part of the procedure is to maximise the model transparency by simplifying the fuzzy linguistic descriptions of the input variables. The rule base evolves over time, utilising direct calculation approaches and hence minimising the reliance on the use of computationally expensive techniques, such as genetic algorithms. An online version of subtractive clustering recently introduced by the authors (P.P. Angelov and R.A. Buswell) is used for determination of the antecedent part of the fuzzy rules. Recursive least squares estimation is employed to determine the parameters of the consequent part of each rule. The use of these efficient non-iterative techniques is the key to the low computational demands of the algorithm. The application of similarity measures improves the interpretability and compactness of the resulting eR model, with no significant detriment to the model precision. A time series prediction problem on data from a real indoor climate control (ICC) system has been considered to test and validate the proposed model simplification method.