Evolution computation based learning algorithms of polygonal fuzzy neural networks
We present two fuzzy conjugate gradient learning algorithms based on evolutionary algorithms for polygonal fuzzy neural networks (PFNN). First, we design a new algorithm, fuzzy conjugate algorithm based on genetic algorithm (GA). In the algorithm, we obtain an optimal learning constant η by GA and t...
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| Published in | International journal of intelligent systems Vol. 26; no. 4; pp. 340 - 352 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken
Wiley Subscription Services, Inc., A Wiley Company
01.04.2011
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| ISSN | 0884-8173 1098-111X 1098-111X |
| DOI | 10.1002/int.20469 |
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| Abstract | We present two fuzzy conjugate gradient learning algorithms based on evolutionary algorithms for polygonal fuzzy neural networks (PFNN). First, we design a new algorithm, fuzzy conjugate algorithm based on genetic algorithm (GA). In the algorithm, we obtain an optimal learning constant η by GA and the experiment indicates the new algorithm always converges. Because the algorithm based on GA is a little slow in every iteration step, we propose to get the learning constant η by quantum genetic algorithm (QGA) in place of GA to decrease time spent in every iteration step. The PFNN tuned by the proposed learning algorithm is applied to approximation realization of fuzzy inference rules, and some experiments demonstrate the whole process. © 2011 Wiley Periodicals, Inc. |
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| AbstractList | We present two fuzzy conjugate gradient learning algorithms based on evolutionary algorithms for polygonal fuzzy neural networks (PFNN). First, we design a new algorithm, fuzzy conjugate algorithm based on genetic algorithm (GA). In the algorithm, we obtain an optimal learning constant Delta *h by GA and the experiment indicates the new algorithm always converges. Because the algorithm based on GA is a little slow in every iteration step, we propose to get the learning constant Delta *h by quantum genetic algorithm (QGA) in place of GA to decrease time spent in every iteration step. The PFNN tuned by the proposed learning algorithm is applied to approximation realization of fuzzy inference rules, and some experiments demonstrate the whole process. ? 2011 Wiley Periodicals, Inc. We present two fuzzy conjugate gradient learning algorithms based on evolutionary algorithms for polygonal fuzzy neural networks (PFNN). First, we design a new algorithm, fuzzy conjugate algorithm based on genetic algorithm (GA). In the algorithm, we obtain an optimal learning constant η by GA and the experiment indicates the new algorithm always converges. Because the algorithm based on GA is a little slow in every iteration step, we propose to get the learning constant η by quantum genetic algorithm (QGA) in place of GA to decrease time spent in every iteration step. The PFNN tuned by the proposed learning algorithm is applied to approximation realization of fuzzy inference rules, and some experiments demonstrate the whole process. © 2011 Wiley Periodicals, Inc. |
| Author | He, Chunmei Ye, Youpei |
| Author_xml | – sequence: 1 givenname: Chunmei surname: He fullname: He, Chunmei email: xiaoxiao_he8@163.com organization: School of Information Engineering, East China Jiaotong University, Nanchang 330013, People's Republic of China – sequence: 2 givenname: Youpei surname: Ye fullname: Ye, Youpei organization: School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, People's Republic of China |
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| References_xml | – reference: Levente FA, Razvan A, Catharine JC, Sarah AW, Nicholas S. A genetic algorithm optimized fuzzy neural network analysis of the affinity of inhibitors for HIV-1 protease. Bioorg Med Chem 2008, 16:2903-2911. – reference: Cordon O, Gomide F, Herrera F, Homann F, Magdalen L. Ten years of genetic fuzzy systems: current framework and new trends. Fuzzy Sets Syst 2004, 141:5-31. – reference: Hsu CF, Cheng KH. Recurrent fuzzy-neural approach for nonlinear control using dynamic structure learning scheme. Neurocomputing. 2008, 71:3447-3459. – reference: Feng SH, Li HX. A new training algorithm for HHFNN based on Gaussian membership function for approximation. Neurocomputing 2009, 72:1631-1638. – reference: Liu PY. A new fuzzy neural network and its approximation capabilities. Sci China (Sr E) 2002, 32(1):76-86. – reference: Li PC, Li SY. Quantum-inspired evolutionary algorithms for continuous space optimization based on Bloch coordinates of qubits. Neurocomputing 2008, 1(4):1-11. – reference: He CM, Ye YP, Xu WH. Universal approximation of fuzzy functions by polygonal fuzzy neural networks. Comput Appl 2008, 28(6):1555-1558. – reference: Liu PY, Li HX. Approximation analysis of feedforward regular fuzzy neural network with two hidden layers. Fuzzy Sets Syst 2005, 150:373-396. – reference: Han KH, Kim JH. Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Trans Evolution Comput 2002, 6(6):580-593. – reference: Gang L, Girijesh P. An on-line algorithm for creating self-organizing fuzzy neural networks. Neural Netw 2004, 17:1477-1493. – reference: Lin CJ. A GA-based neural fuzzy system for temperature control. Fuzzy Sets Syst 2004, 143:311-333. – reference: Huang H, Wu CX. Approximation capabilities of multilayer fuzzy neural networks on the set of fuzzy-valued functions. Inform Sci 2009, 179:2762-2773. – reference: Holland JH. Adaptation in natural and artificial systems. 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Neural Netw doi: 10.1016/j.neunet.2004.07.009 – volume: 141 start-page: 5 year: 2004 ident: 10.1002/int.20469-BIB14|cit14 article-title: Ten years of genetic fuzzy systems: current framework and new trends publication-title: Fuzzy Sets Syst doi: 10.1016/S0165-0114(03)00111-8 – volume-title: Fuzzy neural networks' theory and application research year: 2002 ident: 10.1002/int.20469-BIB10|cit10 – ident: 10.1002/int.20469-BIB15|cit15 doi: 10.1109/ICVD.1999.745212 – volume-title: Adaptation in natural and artificial systems year: 1992 ident: 10.1002/int.20469-BIB11|cit11 doi: 10.7551/mitpress/1090.001.0001 |
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| Title | Evolution computation based learning algorithms of polygonal fuzzy neural networks |
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