Evolved neural network ensemble by multiple heterogeneous swarm intelligence

doi:10.1016/j.neucom.2013.12.062

	Evolved neural network ensemble by multiple heterogeneous swarm intelligence
	Zhao, Zeng-Shun 1,3; Feng, Xiang 1; Lin, Yan-yan 1; Wei, Fang 1; Wang, Shi-Ku 1; Xiao, Tong-Lu 1; Cao, Mao-Yong 1; Hou, Zeng-Guang2
发表期刊	NEUROCOMPUTING
	2015-02-03
卷号	149 期号:A 页码:29-38
文章类型	Article
摘要	The neural network ensemble (NINE) is a very effective way to obtain a good prediction performance by combining the outputs of several independently trained neural networks. Swarm intelligence is applied here to model the population of interacting agents or swarms that are able to self-organize. In this paper, we combine NNE and multi-population swarm intelligence to construct our improved neural network ensemble (INNE). First, each component forward neural network (FNN) is optimized by chaotic particle swarm optimization (CPSO) and gradient gescending (GD) algorithm. Second, in contrast to most existing NNE training algorithm, we adopt multiple obviously different populations to construct swarm intelligence. As an example, one population is trained by particle swarm optimization (PSO) and the others are trained by differential evolution (DE) or artificial bee colony algorithm (ABC). The ensemble weights are trained by multi-population co-evolution PSO-ABC-DE chaotic searching algorithm (M-PSO-ABC-DE-CS). Our experiments demonstrate that the proposed novel INNE algorithm is superior to existing popular NNE in function prediction. (C) 2014 Elsevier B.V. All rights reserved.
关键词	Neural Network Ensemble Particle Swarm Optimization Differential Evolution Artificial Bee Colony Chaotic Search
WOS标题词	Science & Technology ; Technology
DOI	10.1016/j.neucom.2013.12.062
关键词[WOS]	COLONY ABC ALGORITHM ; DIFFERENTIAL EVOLUTION ; OPTIMIZATION
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000360028800005
引用统计	被引频次：17[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/8957
专题	复杂系统认知与决策实验室_先进机器人
作者单位	1.Shandong Univ Sci & Technol, Coll Elect Commun & Phys, Qingdao 266590, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Shandong Univ, Sch Control Sci & Engn, Jinan 250061, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Zeng-Shun,Feng, Xiang,Lin, Yan-yan,et al. Evolved neural network ensemble by multiple heterogeneous swarm intelligence[J]. NEUROCOMPUTING,2015,149(A):29-38.
APA	Zhao, Zeng-Shun.,Feng, Xiang.,Lin, Yan-yan.,Wei, Fang.,Wang, Shi-Ku.,...&Hou, Zeng-Guang.(2015).Evolved neural network ensemble by multiple heterogeneous swarm intelligence.NEUROCOMPUTING,149(A),29-38.
MLA	Zhao, Zeng-Shun,et al."Evolved neural network ensemble by multiple heterogeneous swarm intelligence".NEUROCOMPUTING 149.A(2015):29-38.

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
86-Evolved neural ne（1108KB）	期刊论文	作者接受稿	开放获取	CC BY-NC-SA	浏览下载