Evolved neural network ensemble by multiple heterogeneous swarm intelligence
文献类型：期刊论文
| 作者 | Zhao, Zeng-Shun1,3; Feng, Xiang1; Lin, Yan-yan1; Wei, Fang1; Wang, Shi-Ku1; Xiao, Tong-Lu1; Cao, Mao-Yong1; Hou, Zeng-Guang2
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| 刊名 | NEUROCOMPUTING
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| 出版日期 | 2015-02-03 |
| 卷号 | 149页码:29-38 |
| 关键词 | Neural network ensemble Particle swarm optimization Differential evolution Artificial bee colony Chaotic search |
| 英文摘要 | 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. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence |
| 研究领域[WOS] | Computer Science |
| 关键词[WOS] | COLONY ABC ALGORITHM ; DIFFERENTIAL EVOLUTION ; OPTIMIZATION |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000360028800005 |
| 公开日期 | 2015-12-24 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/8957]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队 |
| 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,2015,149:29-38. |
| 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,149,29-38. |
| MLA | Zhao, Zeng-Shun,et al."Evolved neural network ensemble by multiple heterogeneous swarm intelligence".NEUROCOMPUTING 149(2015):29-38. |
入库方式： OAI收割
来源：自动化研究所
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