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2007 International Conference on Computational Intelligence and Security (CIS 2007)
Dynamic System Modeling with Multilayer Recurrent Fuzzy Neural Network
Harbin, Heilongjiang, China
December 15-December 19
ISBN: 0-7695-3072-9
| ASCII Text | x | ||
| He Liu, Dao Huang, Li Jia, "Dynamic System Modeling with Multilayer Recurrent Fuzzy Neural Network," 2012 Eighth International Conference on Computational Intelligence and Security, pp. 570-574, 2007 International Conference on Computational Intelligence and Security (CIS 2007), 2007. | |||
| BibTex | x | ||
| @article{ 10.1109/CIS.2007.34, author = {He Liu and Dao Huang and Li Jia}, title = {Dynamic System Modeling with Multilayer Recurrent Fuzzy Neural Network}, journal ={2012 Eighth International Conference on Computational Intelligence and Security}, volume = {0}, year = {2007}, isbn = {0-7695-3072-9}, pages = {570-574}, doi = {http://doi.ieeecomputersociety.org/10.1109/CIS.2007.34}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - 2012 Eighth International Conference on Computational Intelligence and Security TI - Dynamic System Modeling with Multilayer Recurrent Fuzzy Neural Network SN - 0-7695-3072-9 SP570 EP574 A1 - He Liu, A1 - Dao Huang, A1 - Li Jia, PY - 2007 VL - 0 JA - 2012 Eighth International Conference on Computational Intelligence and Security ER - | |||
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CIS.2007.34
A multilayer recurrent fuzzy neural network (MRFNN) is proposed for dynamic system modeling in this paper. The proposed MRFNN has six layers combined with T-S fuzzy model. The recurrent structures are formed by local feedback connections in the membership layer and the rule layer. With these feedbacks, the fuzzy sets are time-varying and the temporal problem of dynamic system can be solved well. The parameters of MRFNN are learned by modified chaotic search (CS) and least square estimation (LSE) simultaneously, where CS is for tuning the premise parameters and LSE is for updating the consequent coefficients accordingly. Simulation results of chaos system identification show the proposed approach is effective for dynamic system modeling with high accuracy. And then the proposed approach is applied to a batch reactor modeling.
Citation:
He Liu, Dao Huang, Li Jia, "Dynamic System Modeling with Multilayer Recurrent Fuzzy Neural Network," cis, pp.570-574, 2007 International Conference on Computational Intelligence and Security (CIS 2007), 2007
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