A LEARNING FUZZY NETWORK AND ITS APPLICATIONS TO INVERTED PENDULUM SYSTEM

In this paper, we propose a learning Fuzzy network (LFN) which can be used to implement most of fuzzy logic functions and is much available for hardware implementations. A learning algorithm largely borrowed fr om back propagation algorithm is introduced and used to train the LFN systems for several typical fuzzy logic problems. We also demonstrate the availability of the LFN hardware implementations by realizing them with CMOS current-mode circuits and the capability of the LFN systems by testing them on a benchmark problem in intelligent control-the inv erted pendulum system. Simulations show that a learning fuzzy network can be realized with the proposed LFN system, learning algorithm, and hardware implementations.