Fuzzy-Neural Computing Systems: Recent Developments and Future Directions

Recently, several significant advances have been made in two distinct theoretical areas. These theoretical advances have created an innovative field of theoretical and applied interest: fuzzy neural systems. Researchers have provided a theoretical basis in the field while industry has used this theoretical basis to create a new class of machines using the innovative technology of fuzzy neural networks. The theory of fuzzy logic provides a mathematical framework for capturing the uncertainties associated with human cognitive processes, such as thinking and reasoning. It also provides a mathematical morphology for emulating certain perceptual and linguistic attributes associated with human cognition. On the other hand, computational neural network paradigms have evolved in the process of understanding the incredible learning and adaptive features of neuronal mechanisms inherent in certain biological species. The integration of these two fields, fuzzy logic and neural networks, has the potential for combining the benefits of these two fascinating fields into a single capsule. The intent of this paper is to describe the basic notions of biological and computational neuronal morphologies, and to describe the principles and architectures of fuzzy neural networks.