MODULAR NETWORKS, AND HYBRID LEARNING FUZZY MODELS - Texas A&M University (TAMU) Scholar

This paper proposes a new approach that can integrate fuzzy logic and neural networks in a 'natural' manner. Unlike most existing fuzzy-neural models which usually makes use of the structure of feedforward multilayer networks, the proposed model takes advantage of the structure of a kind of modular networks. We show that fuzzy models have a direct correspondence with the modular networks. Based on this correspondence, we develop an efficient hybrid learning scheme which combines an unsupervised learning algorithm (fuzzy-c-means algorithm) and a supervised algorithm (LMS algorithm). The utility of the proposed approach is illustrated using the well-known Zimmermann and Zysno data[36].

FUZZY MODELS, MODULAR NETWORKS, AND HYBRID LEARNING Conference Paper