Fuzzy Neural Networks Design Methods Based on Swarm Intelligent Optimization Algorithms and Its Application

The structure and parameters of fuzzy neural networks (FNN) are analyzed and a Boolean variable is proposed to network as the structure parameter. Then the question of FNN design is transformed to a function optimization question with multi-parameters. A new hybrid swarm intelligent optimization alg...

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Autor principal:	Wang Yonghai,Guo Ke,Fang Yue,Ye Yuling
Formato:	article
Lenguaje:	ZH
Publicado:	Editorial Office of Aero Weaponry 2021
Materias:	\|fuzzy neural network (fnn)\|swarm intelligent optimization algorithm\|cooperative optimization\|least square error algorithm\|structure of network Motor vehicles. Aeronautics. Astronautics TL1-4050
Acceso en línea:	https://doaj.org/article/b0f7b60cc1584518a746af3434488b46
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id	oai:doaj.org-article:b0f7b60cc1584518a746af3434488b46
record_format	dspace
spelling	oai:doaj.org-article:b0f7b60cc1584518a746af3434488b462021-11-30T00:13:57ZFuzzy Neural Networks Design Methods Based on Swarm Intelligent Optimization Algorithms and Its Application1673-504810.12132/ISSN.1673-5048.2019.0254https://doaj.org/article/b0f7b60cc1584518a746af3434488b462021-02-01T00:00:00Zhttp://www.aeroweaponry.avic.com/fileup/1673-5048/PDF/2019-00254.pdfhttps://doaj.org/toc/1673-5048The structure and parameters of fuzzy neural networks (FNN) are analyzed and a Boolean variable is proposed to network as the structure parameter. Then the question of FNN design is transformed to a function optimization question with multi-parameters. A new hybrid swarm intelligent optimization algorithm is proposed, and its binary-coded form (BIOA) and real-coded form (RIOA) are presented. BIOA and RIOA are applied to cooperative optimize the structure parameters and premise parameters of the FNN. The conclusion parameters of the FNN are optimized by least square error algorithm after the premise parameters are obtained. In the experiment, the number of sunspots is modeled by FNN and the results show that the designed FNN not only has simpler structure, but has higher precision and generalization.Wang Yonghai,Guo Ke,Fang Yue,Ye YulingEditorial Office of Aero Weaponryarticle\|fuzzy neural network (fnn)\|swarm intelligent optimization algorithm\|cooperative optimization\|least square error algorithm\|structure of networkMotor vehicles. Aeronautics. AstronauticsTL1-4050ZHHangkong bingqi, Vol 28, Iss 1, Pp 87-92 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	\|fuzzy neural network (fnn)\|swarm intelligent optimization algorithm\|cooperative optimization\|least square error algorithm\|structure of network Motor vehicles. Aeronautics. Astronautics TL1-4050
spellingShingle	\|fuzzy neural network (fnn)\|swarm intelligent optimization algorithm\|cooperative optimization\|least square error algorithm\|structure of network Motor vehicles. Aeronautics. Astronautics TL1-4050 Wang Yonghai,Guo Ke,Fang Yue,Ye Yuling Fuzzy Neural Networks Design Methods Based on Swarm Intelligent Optimization Algorithms and Its Application
description	The structure and parameters of fuzzy neural networks (FNN) are analyzed and a Boolean variable is proposed to network as the structure parameter. Then the question of FNN design is transformed to a function optimization question with multi-parameters. A new hybrid swarm intelligent optimization algorithm is proposed, and its binary-coded form (BIOA) and real-coded form (RIOA) are presented. BIOA and RIOA are applied to cooperative optimize the structure parameters and premise parameters of the FNN. The conclusion parameters of the FNN are optimized by least square error algorithm after the premise parameters are obtained. In the experiment, the number of sunspots is modeled by FNN and the results show that the designed FNN not only has simpler structure, but has higher precision and generalization.
format	article
author	Wang Yonghai,Guo Ke,Fang Yue,Ye Yuling
author_facet	Wang Yonghai,Guo Ke,Fang Yue,Ye Yuling
author_sort	Wang Yonghai,Guo Ke,Fang Yue,Ye Yuling
title	Fuzzy Neural Networks Design Methods Based on Swarm Intelligent Optimization Algorithms and Its Application
title_short	Fuzzy Neural Networks Design Methods Based on Swarm Intelligent Optimization Algorithms and Its Application
title_full	Fuzzy Neural Networks Design Methods Based on Swarm Intelligent Optimization Algorithms and Its Application
title_fullStr	Fuzzy Neural Networks Design Methods Based on Swarm Intelligent Optimization Algorithms and Its Application
title_full_unstemmed	Fuzzy Neural Networks Design Methods Based on Swarm Intelligent Optimization Algorithms and Its Application
title_sort	fuzzy neural networks design methods based on swarm intelligent optimization algorithms and its application
publisher	Editorial Office of Aero Weaponry
publishDate	2021
url	https://doaj.org/article/b0f7b60cc1584518a746af3434488b46
work_keys_str_mv	AT wangyonghaiguokefangyueyeyuling fuzzyneuralnetworksdesignmethodsbasedonswarmintelligentoptimizationalgorithmsanditsapplication
_version_	1718406884764942336

Fuzzy Neural Networks Design Methods Based on Swarm Intelligent Optimization Algorithms and Its Application

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