Unification of neural and wavelet networks and fuzzy systems

This paper analyzes several commonly used soft computing paradigms (neural and wavelet networks and fuzzy systems, Bayesian classifiers, fuzzy partitions, etc.) and tries to outline similarities and differences among each other. These are exploited to produce the weighted radial basis functions paradigm which may act as a neuro-fuzzy unification paradigm. Training rules (both supervised and unsupervised) are also unified by the proposed algorithm. Analyzing differences and similarities among existing paradigms helps to understand that many soft computing paradigms are very similar to each other and can be grouped in just two major classes. The many reasons to unify soft computing paradigms are also shown in the paper. A conversion method is presented to convert perceptrons, radial basis functions, wavelet networks, and fuzzy systems from each other.