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

Imran Dawy; Tian Songya

doi:doi:10.11648/j.ajai.20180201.11

| Peer-Reviewed

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

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

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), 2024. Published by Science Publishing Group

Keywords

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

References

[1]	L. A. Zadeh; “Fuzzy Sets”, Information and Control, 1965, Vol. 8, pp. 338-353.
[2]	Mamdani, E. H. and S. Assilian, "An experiment in linguistic synthesis with a fuzzy logic controller," International Journal of Man-Machine Studies, Vol. 7, No. 1, pp.1-13, 1975.
[3]	Lee S. C. and Lee E. T. (1970): Fuzzy neurons and automata, Proceedings of the 4th Princeton Conference on Information Science Systems, pp.381-385.
[4]	Lee S. C. and Lee E. T. (1974): Fuzzy sets and neural networks, Journal of Cybernetics, Vol. 4, pp.83-103.
[5]	Lee S. C. and Lee E. T. (1975): Fuzzy neural networks, Mathematical Bioscienses, Vol. 23, pp.151-177.
[6]	Butnariu D. (1977): L-fuzzy automata. Description of a neural model, Proceedings of the International Congress on Cybernetics and Sys-tems, Bucharest, Romania, Vol. 3, No.2, pp. 119-124.
[7]	Rocha A. F. (1981): Neural fuzzy point processes, Fuzzy Sets and Systems, Vol. 5, No.2, pp.127-140.
[8]	Canuto A., Howells G., and Fairhurst M. (1999): RePART: a modified fuzzy ARTMAP for pattern recognition, In: Reusch B. (Ed.), Com-putational Intelligence. Theory and Applications, Proceedings of the International Conference: 6th Fuzzy Days, Dortmund, Germany, Lec-ture Notes in Computer Science, Vol. 1625, pp. 159-168.
[9]	Nauck D., Klawonn F., and Kruse R. (1997): Foundations of Neuro-Fuzzy Systems, John Wiley & Sons.
[10]	Mikhail Z. Zgurovsky., and Yuriy P. Zaychenko (2017): The Fundamentals of Computational Intelligence: System Approach, Springer International Publishing Switzerland.
[11]	Altrock, V. Kruse, B., and Zimmermann, H. (1992) “Advance fuzzy logic controller’s techniques in automobile applications,” in Proceedings of IEEE International Conference on Fuzzy Systems, pp. 835–42.
[12]	Wang, L., and Yen, J. (1999) “Extracting fuzzy rules for system modeling using a hybrid of genetic algorithms and Kalman ﬁlter,” Fuzzy Sets and Systems, vol. 101, no. 3, pp. 353–62.
[13]	Hayashi, I. Nomura, H. Yamasaki, H., and Wakami, N. (1992) “Construction of fuzzy inference rules by NDF and NDFL,” International Journal of Approximate Reasoning, vol. 6, pp. 241–66.
[14]	D’Alche-Buc, F., Andres, V., and Nadal, J. P. (1994) “Rule extraction with fuzzyneural network,” International Journal of Neural Systems, vol. 5, no. 1, pp. 1–11, March.
[15]	Takagi, H., and Hayashi, I. (1991) “A neural network-driven fuzzy reasoning,” International Journal of Approximate Reasoning, vol. 5, no. 3, pp. 191–212.
[16]	Juang, F., and Lin, C. T. (1998) “An on line self-construction neural fuzzy infer-ence network and its applications,” IEEE Transactions on Fuzzy Systems, vol. 6, no. 1, pp. 12–32.
[17]	Maguire, L. P., McGinnity, T. M., and McDaid, L. J. (1997) “A fuzzy neural net-work for approximate fuzzy reasoning,” in Intelligent Hybrid Systems, edited by Da Ruan, Kluwer Academic Publisher, MA.
[18]	Jang, J. R. (1992) “ANFIS: adaptive-network-based fuzzy inference systems,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 23, no. 3, pp. 665–85.
[19]	Jun Zhang, G. G. Walter, Y. Miao and Wan Ngai Wayne Lee, "Wavelet neural networks for function learning," in IEEE Transactions on Signal Processing, vol. 43, no. 6, pp. 1485-1497, Jun 1995.
[20]	Q. Zhang and A. Benveniste, "Wavelet networks," in IEEE Transactions on Neural Networks, vol. 3, no. 6, pp. 889-898, Nov 1992.
[21]	C. H. Lu, "Wavelet Fuzzy Neural Networks for Identification and Predictive Control of Dynamic Systems," in IEEE Transactions on Industrial Electronics, vol. 58, no. 7, pp. 3046-3058, July 2011.
[22]	R. H. Abiyev and O. Kaynak, "Fuzzy Wavelet Neural Networks for Identification and Control of Dynamic Plants—A Novel Structure and a Comparative Study," in IEEE Transactions on Industrial Electronics, vol. 55, no. 8, pp. 3133-3140, Aug. 2008.
[23]	M. Davanipour, M. Zekri and F. Sheikholeslam, "The preference of Fuzzy Wavelet Neural Network to ANFIS in identification of nonlinear dynamic plants with fast local variation," 2010 18th Iranian Conference on Electrical Engineering, Isfahan, 2010, pp. 605-609.

Cite This Article

Plain Text BibTeX RIS

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

Copy | Download

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  -

Copy | Download

Author Information

Imran Dawy

Faculty of Mechanical & Electrical Engineering, Hohai University, Changzhou, China
Tian Songya

Faculty of Mechanical & Electrical Engineering, Hohai University, Changzhou, China

Download PDF

Sections

Plain Text BibTeX RIS

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

Copy | Download

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  -

Copy | Download