Optimizing the Structure of Radial Basis Function Networks by Optimizing Fuzzy Inference Systems with Evolution Strategy Internal Report 93{07

This report takes advantage of Neural Networks (NN) and Fuzzy Inference Systems (FIS) in order to design a system suited to predict time series. We choose the solution of the Mackey{Glass time delay diierential equation in the chaotic domain as a sample problem. Fuzzy rules are generated from the sample data. The system performance is improved by means of Evolution Strategy (ES). The rules of the FIS are diminished in number due to a heuristic approach. The optimization process is convenient for the structural design of Radial Basis Function Networks (RBFN). The so far predetermined RBFN is further optimized by gradient descent. The system exhibits a good prediction accuracy and generalization.

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