Synergistic modeling and applications of hierarchical fuzzy neural networks

Many common foundations exist between neural networks and fuzzy inference systems in terms of their mathematical models and system structures. This paper explores such a rich synergy and uses it to form the basis for a unifying framework under which fuzzy logic processing and neural networks may be integrated to achieve more robust information processing. It in turn leads to a family of hierarchical fuzzy neural networks (FNNs) which incorporate an adaptive and modular design of neural networks into the basic fuzzy logic systems. Several important models which are critical to the development of the the hierarchical FNN family are studied. We demonstrate how existing unsupervised and supervised learning strategies can be an integral part of a fuzzy processing framework. In addition, hierarchical structures involving both expert modules and class modules are incorporated into the FNNs. Also presented are some promising application examples.