A neural fuzzy approach for fuzzy system design

A new class of neural fuzzy network based on a general neuron model is introduced in this paper. The network encodes a fuzzy rule base in its structure and process data according to fuzzy reasoning schemes. It learns membership functions for each input variable and rules covering the whole input/output space. These are important decisions when designing fuzzy systems. Due to its structure the fuzzy rules encoded are trivially and explicitly recovered. The network is also shown to have the universal approximation capability. Simulation results are included to compare its features with alternative approaches and to show its usefulness as well. For the function approximation problem, the neural fuzzy network developed here has shown to be superior from the accuracy, complexity, and system design point of view.