Real-time supervised structure/parameter learning for fuzzy neural network

The authors propose a real-time supervised structure and parameter learning algorithm for constructing fuzzy neural networks (FNNs) automatically and dynamically. This algorithm combines the backpropagation learning scheme for the parameter learning and a novel fuzzy similarity measure for the structure learning. The fuzzy similarity measure is a new tool to determine the degree to which two fuzzy sets are equal. The FNN is a feedforward multilayered network which integrates the basic elements and functions of a traditional fuzzy logic controller into a connectionist structure which has distributed learning abilities. The structure learning decides the proper connection types and the number of hidden units which represent fuzzy logic rules and the number of fuzzy partitions. The parameter learning adjusts the node and link parameters which represent the membership functions. The proposed supervised learning algorithm provides an efficient way of constructing a FNN in real time. Simulation results are presented to illustrate the performance and applicability of the proposed learning algorithm