Genetic Dynamic Fuzzy Neural Network (GDFNN) for nonlinear system identification

Mahardhika Pratama; Meng Joo Er; Xiang Li; Lin San; J. O. Richard; L. Y. Zhai; Amin Torabi; Imam Arifin

doi:10.1007/978-3-642-21090-7_61

Genetic Dynamic Fuzzy Neural Network (GDFNN) for nonlinear system identification

Mahardhika Pratama^*, Meng Joo Er, Xiang Li, Lin San, J. O. Richard, L. Y. Zhai, Amin Torabi, Imam Arifin

^*Corresponding author for this work

Laboratory of Cyber Physical, Industrial Robotics, and Automation

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

This paper discusses an optimization of Dynamic Fuzzy Neural Network (DFNN) for nonlinear system identification. DFNN has 10 parameters which are proved sensitive to the performance of that algorithm. In case of not suitable parameters, the result gives undesirable of the DFNN. In the other hand, each of problems has different characteristics such that the different values of DFNN parameters are necessary. To solve that problem is not able to be approached with trial and error, or experiences of the experts. Therefore, more scientific solution has to be proposed thus DFNN is more user friendly, Genetic Algorithm overcomes that problems. Nonlinear system identification is a common testing of Fuzzy Neural Network to verify whether FNN might achieve the requirement or not. The Experiments show that Genetic Dynamic Fuzzy Neural Network Genetic (GDFNN) exhibits the best result which is compared with other methods.

Original language	English
Title of host publication	Advances in Neural Networks - 8th International Symposium on Neural Networks, ISNN 2011
Pages	525-534
Number of pages	10
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-21090-7_61
Publication status	Published - 2011
Event	8th International Symposium on Neural Networks, ISNN 2011 - Guilin, China Duration: 29 May 2011 → 1 Jun 2011

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	6676 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	8th International Symposium on Neural Networks, ISNN 2011
Country/Territory	China
City	Guilin
Period	29/05/11 → 1/06/11

Keywords

Dynamic Fuzzy Neural Network
Fuzzy Neural Network
Genetic Algorithm
Genetic Dynamic Fuzzy Neural Network

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10.1007/978-3-642-21090-7_61

Cite this

Pratama, M., Er, M. J., Li, X., San, L., Richard, J. O., Zhai, L. Y., Torabi, A., & Arifin, I. (2011). Genetic Dynamic Fuzzy Neural Network (GDFNN) for nonlinear system identification. In Advances in Neural Networks - 8th International Symposium on Neural Networks, ISNN 2011 (PART 2 ed., pp. 525-534). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6676 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-21090-7_61

Pratama, Mahardhika ; Er, Meng Joo ; Li, Xiang et al. / Genetic Dynamic Fuzzy Neural Network (GDFNN) for nonlinear system identification. Advances in Neural Networks - 8th International Symposium on Neural Networks, ISNN 2011. PART 2. ed. 2011. pp. 525-534 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

@inproceedings{e0657275a8d3449086d4df8115ff44b3,

title = "Genetic Dynamic Fuzzy Neural Network (GDFNN) for nonlinear system identification",

abstract = "This paper discusses an optimization of Dynamic Fuzzy Neural Network (DFNN) for nonlinear system identification. DFNN has 10 parameters which are proved sensitive to the performance of that algorithm. In case of not suitable parameters, the result gives undesirable of the DFNN. In the other hand, each of problems has different characteristics such that the different values of DFNN parameters are necessary. To solve that problem is not able to be approached with trial and error, or experiences of the experts. Therefore, more scientific solution has to be proposed thus DFNN is more user friendly, Genetic Algorithm overcomes that problems. Nonlinear system identification is a common testing of Fuzzy Neural Network to verify whether FNN might achieve the requirement or not. The Experiments show that Genetic Dynamic Fuzzy Neural Network Genetic (GDFNN) exhibits the best result which is compared with other methods.",

keywords = "Dynamic Fuzzy Neural Network, Fuzzy Neural Network, Genetic Algorithm, Genetic Dynamic Fuzzy Neural Network",

author = "Mahardhika Pratama and Er, {Meng Joo} and Xiang Li and Lin San and Richard, {J. O.} and Zhai, {L. Y.} and Amin Torabi and Imam Arifin",

year = "2011",

doi = "10.1007/978-3-642-21090-7_61",

language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

number = "PART 2",

pages = "525--534",

booktitle = "Advances in Neural Networks - 8th International Symposium on Neural Networks, ISNN 2011",

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Pratama, M, Er, MJ, Li, X, San, L, Richard, JO, Zhai, LY, Torabi, A & Arifin, I 2011, Genetic Dynamic Fuzzy Neural Network (GDFNN) for nonlinear system identification. in Advances in Neural Networks - 8th International Symposium on Neural Networks, ISNN 2011. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 6676 LNCS, pp. 525-534, 8th International Symposium on Neural Networks, ISNN 2011, Guilin, China, 29/05/11. https://doi.org/10.1007/978-3-642-21090-7_61

Genetic Dynamic Fuzzy Neural Network (GDFNN) for nonlinear system identification. / Pratama, Mahardhika; Er, Meng Joo; Li, Xiang et al.
Advances in Neural Networks - 8th International Symposium on Neural Networks, ISNN 2011. PART 2. ed. 2011. p. 525-534 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6676 LNCS, No. PART 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Genetic Dynamic Fuzzy Neural Network (GDFNN) for nonlinear system identification

AU - Pratama, Mahardhika

AU - Er, Meng Joo

AU - Li, Xiang

AU - San, Lin

AU - Richard, J. O.

AU - Zhai, L. Y.

AU - Torabi, Amin

AU - Arifin, Imam

PY - 2011

Y1 - 2011

N2 - This paper discusses an optimization of Dynamic Fuzzy Neural Network (DFNN) for nonlinear system identification. DFNN has 10 parameters which are proved sensitive to the performance of that algorithm. In case of not suitable parameters, the result gives undesirable of the DFNN. In the other hand, each of problems has different characteristics such that the different values of DFNN parameters are necessary. To solve that problem is not able to be approached with trial and error, or experiences of the experts. Therefore, more scientific solution has to be proposed thus DFNN is more user friendly, Genetic Algorithm overcomes that problems. Nonlinear system identification is a common testing of Fuzzy Neural Network to verify whether FNN might achieve the requirement or not. The Experiments show that Genetic Dynamic Fuzzy Neural Network Genetic (GDFNN) exhibits the best result which is compared with other methods.

AB - This paper discusses an optimization of Dynamic Fuzzy Neural Network (DFNN) for nonlinear system identification. DFNN has 10 parameters which are proved sensitive to the performance of that algorithm. In case of not suitable parameters, the result gives undesirable of the DFNN. In the other hand, each of problems has different characteristics such that the different values of DFNN parameters are necessary. To solve that problem is not able to be approached with trial and error, or experiences of the experts. Therefore, more scientific solution has to be proposed thus DFNN is more user friendly, Genetic Algorithm overcomes that problems. Nonlinear system identification is a common testing of Fuzzy Neural Network to verify whether FNN might achieve the requirement or not. The Experiments show that Genetic Dynamic Fuzzy Neural Network Genetic (GDFNN) exhibits the best result which is compared with other methods.

KW - Dynamic Fuzzy Neural Network

KW - Fuzzy Neural Network

KW - Genetic Algorithm

KW - Genetic Dynamic Fuzzy Neural Network

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SN - 9783642210891

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Advances in Neural Networks - 8th International Symposium on Neural Networks, ISNN 2011

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ER -

Pratama M, Er MJ, Li X, San L, Richard JO, Zhai LY et al. Genetic Dynamic Fuzzy Neural Network (GDFNN) for nonlinear system identification. In Advances in Neural Networks - 8th International Symposium on Neural Networks, ISNN 2011. PART 2 ed. 2011. p. 525-534. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-21090-7_61

Genetic Dynamic Fuzzy Neural Network (GDFNN) for nonlinear system identification

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