The Most General Intelligent Architectures of the Hybrid Neuro-Fuzzy Models

Imran Dawy; Tian Songya

doi:doi:10.11648/j.ajai.20180201.11

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The Most General Intelligent Architectures of the Hybrid Neuro-Fuzzy Models

Imran Dawy, Tian Songya

Published in American Journal of Artificial Intelligence (Volume 2, Issue 1)

Received: 25 November 2017 Accepted: 12 December 2017 Published: 5 January 2018

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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.

Published in	American Journal of Artificial Intelligence (Volume 2, Issue 1)
DOI	10.11648/j.ajai.20180201.11
Page(s)	1-6
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Hybrid Architectures, Intelligent System, Cooperative Systems, ANFIS, FWNN

References

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Imran Dawy, Tian Songya. (2018). The Most General Intelligent Architectures of the Hybrid Neuro-Fuzzy Models. American Journal of Artificial Intelligence, 2(1), 1-6. https://doi.org/10.11648/j.ajai.20180201.11

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ACS Style

Imran Dawy; Tian Songya. The Most General Intelligent Architectures of the Hybrid Neuro-Fuzzy Models. Am. J. Artif. Intell. 2018, 2(1), 1-6. doi: 10.11648/j.ajai.20180201.11

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AMA Style

Imran Dawy, Tian Songya. The Most General Intelligent Architectures of the Hybrid Neuro-Fuzzy Models. Am J Artif Intell. 2018;2(1):1-6. doi: 10.11648/j.ajai.20180201.11

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@article{10.11648/j.ajai.20180201.11,
  author = {Imran Dawy and Tian Songya},
  title = {The Most General Intelligent Architectures of the Hybrid Neuro-Fuzzy Models},
  journal = {American Journal of Artificial Intelligence},
  volume = {2},
  number = {1},
  pages = {1-6},
  doi = {10.11648/j.ajai.20180201.11},
  url = {https://doi.org/10.11648/j.ajai.20180201.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20180201.11},
  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.},
 year = {2018}
}

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TY  - JOUR
T1  - The Most General Intelligent Architectures of the Hybrid Neuro-Fuzzy Models
AU  - Imran Dawy
AU  - Tian Songya
Y1  - 2018/01/05
PY  - 2018
N1  - https://doi.org/10.11648/j.ajai.20180201.11
DO  - 10.11648/j.ajai.20180201.11
T2  - American Journal of Artificial Intelligence
JF  - American Journal of Artificial Intelligence
JO  - American Journal of Artificial Intelligence
SP  - 1
EP  - 6
PB  - Science Publishing Group
SN  - 2639-9733
UR  - https://doi.org/10.11648/j.ajai.20180201.11
AB  - 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.
VL  - 2
IS  - 1
ER  -

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Author Information

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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APA Style

Imran Dawy, Tian Songya. (2018). The Most General Intelligent Architectures of the Hybrid Neuro-Fuzzy Models. American Journal of Artificial Intelligence, 2(1), 1-6. https://doi.org/10.11648/j.ajai.20180201.11

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ACS Style

Imran Dawy; Tian Songya. The Most General Intelligent Architectures of the Hybrid Neuro-Fuzzy Models. Am. J. Artif. Intell. 2018, 2(1), 1-6. doi: 10.11648/j.ajai.20180201.11

Copy | Download

AMA Style

Imran Dawy, Tian Songya. The Most General Intelligent Architectures of the Hybrid Neuro-Fuzzy Models. Am J Artif Intell. 2018;2(1):1-6. doi: 10.11648/j.ajai.20180201.11

Copy | Download

@article{10.11648/j.ajai.20180201.11,
  author = {Imran Dawy and Tian Songya},
  title = {The Most General Intelligent Architectures of the Hybrid Neuro-Fuzzy Models},
  journal = {American Journal of Artificial Intelligence},
  volume = {2},
  number = {1},
  pages = {1-6},
  doi = {10.11648/j.ajai.20180201.11},
  url = {https://doi.org/10.11648/j.ajai.20180201.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20180201.11},
  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.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - The Most General Intelligent Architectures of the Hybrid Neuro-Fuzzy Models
AU  - Imran Dawy
AU  - Tian Songya
Y1  - 2018/01/05
PY  - 2018
N1  - https://doi.org/10.11648/j.ajai.20180201.11
DO  - 10.11648/j.ajai.20180201.11
T2  - American Journal of Artificial Intelligence
JF  - American Journal of Artificial Intelligence
JO  - American Journal of Artificial Intelligence
SP  - 1
EP  - 6
PB  - Science Publishing Group
SN  - 2639-9733
UR  - https://doi.org/10.11648/j.ajai.20180201.11
AB  - 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.
VL  - 2
IS  - 1
ER  -

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