A Compensatory NeuroFuzzy System with Online Constructing and Parameter Learning

Abstract

A compensatory neurofuzzy system (CNFS) with on-line learning ability is proposed in this paper. The proposed CNFS model uses a compensatory layer to raise the diversity of fuzzy rules by compensatory weights. The compensatory layer can automatically compare with each fuzzy rule and select higher resources for more important fuzzy rule. An online learning algorithm, which consists of structure learning and parameter learning, is also presented. The structure learning depends on the fuzzy measure to determine the number of fuzzy rules. The parameter learning, based on the gradient descent method, can adjust the shape of the membership function and the weights of the compensatory layer. To demonstrate the capability of the proposed CNFS, it is applied to the Iris, and Wisconsin breast cancer classification datasets from the UCI Repository. Experimental results show that the proposed CNFS for pattern classification can achieve good classification performance.