A self-organizing fuzzified basis function network control system applicable to nonlinear servomechanisms

Abstract

The application and extension of suitable techniques for integrating neural network and fuzzy system methodologies to enhance a basic fixed neural network-based nonlinear control strategy with the property of self-organization are investigated. It is shown that by establishing a suitable correspondence between Radial Basis Function networks and fuzzy systems, it is possible to develop a self-organizing controller, utilizing a class of Fuzzified Basis Function Networks (FBFN), that autonomously organizes its network structure to the required size and parameters. The effectiveness of the proposed self-organizing FBFN control system is demonstrated in real-time implementation experiments for position control in a servomechanism with asymmetrical loading and changes in the load. © 1995.