ABSTRACT
The concept of fuzzy-neural networks was created two decades ago by Lee and Lee (1975) but the recent resurgence of fuzzy-neural systems with a great amount of published works may be motivated by the increasing recognition of the potential of fuzzy logic and neural networks in different areas of theoretical and applied research. At this moment, many researchers are investigating ways to build up fuzzy-neural systems by incorporating fuzziness or fuzzy rules in a neural network, or by applying the computational and learning potentials of neural networks to fuzzy systems. This chapter introduces a first approach to a taxonomy of fuzzy-neural systems. Two different classifications are proposed, the first one based on the symbolic and connectionist elements included in the fuzzy-neural system (Section 2), and the second one based on the application of the system (Section 3). Each class includes bibliographical references, pointing to papers describing fuzzy-neural systems belonging to the class. A brief analysis of the applicability of each kind of fuzzy-neural system is presented in Section 4.
