The Hybrid Neuro-Fuzzy Models' Most General Intelligent Architectures

Abstract

Hybrid systems of the fuzzy logic and neural networks, are widely spread in real world problems with high effectivenessand versatility for different kinds of applications. The state description of unknown plant by using mathematical models, sometimes,is difficult to obtain. The fuzzy logic systems with their ability of tackling imprecise knowledges, and neural networks with theiradvantages of establishing a relationship between the inputs and the outputs of the system, are represented as qualified tools forsystems of unknown plant. Furthermore, the hybrid systems which utilize the features of the fuzzy logic and Neural networks hasbeen employed for better characteristics. Whilst, there are several different architectures of the neuro-fuzzy system proposed inliterature, this article come out to highlight the common known architectures of how these techniques fuse together to build anenhanced system that can complement the lack of each method individually and improve the system performance over all.