An ART-based fuzzy adaptive learning control network

Abstract

This paper addresses the structure and an associated on-line learning algorithm of a feedforward multilayered connectionist network for realizing the basic elements and functions of a traditional fuzzy logic controller. The proposed fuzzy adaptive learning control network (FALCON) can be contrasted with the traditional fuzzy logic control systems in their network structure and learning ability. An on-line structure/parameter learning algorithm called FALCON-ART is proposed for constructing the FALCON dynamically. It combines the backpropagation learning scheme for parameter learning and the fuzzy ART algorithm for structure learning. The FALCON-ART has some important features. First of all, it partitions the input state space and output control space using irregular fuzzy hyperboxes according to the distribution of training data. In many existing fuzzy or neural fuzzy control systems, the input and output spaces are always partitioned into ''grids.'' As the number of input/output variables increases, the number of partitioned grids will grow combinatorially. To avoid the problem of combinatorial growing of partitioned grids in some complex systems, the FALCON-ART partitions the input/output spaces in a flexible way based on the distribution of training data. Second, the FALCON-ART can create and train the FALCON in a highly autonomous way. In its initial form, there is no membership function, fuzzy partition, and fuzzy logic rule. They are created and begin to grow as the first training pattern arrives. Thus, the users need not give it any a priori knowledge or even any initial information on these. More notably, the FALCON-ART can on-line partition the input/output spaces, tune membership functions, find proper fuzzy logic rules, and annihilate redundant rules dynamically upon receiving online incoming training data. Computer simulations have been conducted to illustrate the performance and applicability of the proposed system.