Implementation of fuzzy controllers with radial basis neural networks
A. Little, Leon Reznik · 2002
Low cost microprocessors cannot always devote the resources necessary to compute a fuzzy system, and this can be a deterrent in its application. The purpose of this work is to demonstrate that neural networks are a viable form for implementing fuzzy systems in a practical cost effective application. A neural network can be trained to efficiently approximate a fuzzy control surface to a desired degree of accuracy. The paper proposes a neuro-fuzzy synergetic design procedure consisting of a fuzzy controller design and its implementation with a radial basis function neural network. The trade-offs associated with accuracy, speed and processing requirements are addressed, and the realization results are then presented and discussed.