Designing Optimal Neuro-Fuzzy Architectures for Intelligent Control
Ajith Abraham, Baikunth Nath · 2000
The integration of Artificial Neural Network (ANN) learning and Fuzzy Logic (FL) approximate reasoning in one architecture, to overcome individual limitations and achieve synergetic effects through hybridization of these techniques, has in recent years contributed to a large number of Neuro-Fuzzy (NF) architectures. NF techniques override the classical control methods in many aspects, such as algorithm simplicity, system robustness and the ability to handle imprecision and uncertainty. In this paper we present the state-of-art NF models that have evolved in the past few years. We further attempt to assess the strengths and weakness of each NF architecture and selection criteria for IC applications. Finally we present our vision of an optimal NF architecture and future research directions.