Synthesis of fuzzy controllers through neural networks

Most of the current fuzzy logic control applications are designed using different heuristics for the controller synthesis, and then implemented using conventional programming languages on general purpose microcontrollers. We are proposing a methodology for the design of fuzzy controllers based on the cell-to-cell mapping approach for the fuzzy control law synthesis, and on neural networks for: (a) the discovery of the set of appropriate fuzzy rules that characterise a control law, and (b) the tuning of parameters that characterise membership functions (namely position and width). We suppose that training data are coming from sampling of the analytical control function. Two examples are shown, and a comparison with the results obtained by a generalized multilayer perceptron is discussed.