The Most General Intelligent Architectures of the Hybrid Neuro-Fuzzy Models
American Journal of Artificial Intelligence
Volume 2, Issue 1, June 2018, Pages: 1-6
Received: Nov. 25, 2017; Accepted: Dec. 12, 2017; Published: Jan. 5, 2018
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Authors
Imran Dawy, Faculty of Mechanical & Electrical Engineering, Hohai University, Changzhou, China
Tian Songya, Faculty of Mechanical & Electrical Engineering, Hohai University, Changzhou, China
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Abstract
Hybrid systems of the fuzzy logic and neural networks, are widely spread in real world problems with high effectiveness and 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 their advantages of establishing a relationship between the inputs and the outputs of the system, are represented as qualified tools for systems of unknown plant. Furthermore, the hybrid systems which utilize the features of the fuzzy logic and Neural networks has been employed for better characteristics. Whilst, there are several different architectures of the neuro-fuzzy system proposed in literature, this article come out to highlight the common known architectures of how these techniques fuse together to build an enhanced system that can complement the lack of each method individually and improve the system performance over all.
Keywords
Hybrid Architectures, Intelligent System, Cooperative Systems, ANFIS, FWNN
To cite this article
Imran Dawy, Tian Songya, The Most General Intelligent Architectures of the Hybrid Neuro-Fuzzy Models, American Journal of Artificial Intelligence. Vol. 2, No. 1, 2018, pp. 1-6. doi: 10.11648/j.ajai.20180201.11
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Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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