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A multi-swarm optimizer based fuzzy modeling approach for dynamic systems processing
Niu B(牛奔); Zhu YL(朱云龙); He XX(何小贤); Shen H(申海)
Department先进制造技术研究室
Source PublicationNeurocomputing
ISSN0925-2312
2008
Volume71Issue:7-9Pages:1436-1448
Indexed BySCI ; EI
EI Accession number20081211157202
WOS IDWOS:000255239200029
Contribution Rank1
KeywordDynamical Systems Fuzzy Controller Fuzzy Identifier Particle Swarm Optimization Multi-swarm
AbstractInspired by the phenomenon of symbiosis in natural ecosystems a multi-swarm cooperative particle swarm optimizer (MCPSO) is proposed as a new fuzzy modeling strategy for identification and control of non-linear dynamical systems. In MCPSO, the population consists of one master swarm and several slave swarms. The slave swarms execute particle swarm optimization (PSO) or its variants independently to maintain the diversity of particles, while the particles in the master swarm enhance themselves based on their own knowledge and also the knowledge of the particles in the slave swarms. With four benchmark functions, MCPSO is proved to have better performance than PSO and its variants. MCPSO is then used to automatically design the fuzzy identifier and fuzzy controller for non-linear dynamical systems. The proposed algorithm (MCPSO) is shown to outperform PSO and some other methods in identifying and controlling dynamical systems.
Language英语
WOS HeadingsScience & Technology ; Technology
WOS SubjectComputer Science, Artificial Intelligence
WOS KeywordRECURRENT NEURAL-NETWORK ; PARTICLE SWARM ; HYBRID ALGORITHM ; IDENTIFICATION ; DESIGN
WOS Research AreaComputer Science
Citation statistics
Cited Times:31[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/6923
Collection工业信息学研究室_先进制造技术研究室
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Graduate School, the Chinese Academy of Sciences, Beijing 100049, China
Recommended Citation
GB/T 7714
Niu B,Zhu YL,He XX,et al. A multi-swarm optimizer based fuzzy modeling approach for dynamic systems processing[J]. Neurocomputing,2008,71(7-9):1436-1448.
APA Niu B,Zhu YL,He XX,&Shen H.(2008).A multi-swarm optimizer based fuzzy modeling approach for dynamic systems processing.Neurocomputing,71(7-9),1436-1448.
MLA Niu B,et al."A multi-swarm optimizer based fuzzy modeling approach for dynamic systems processing".Neurocomputing 71.7-9(2008):1436-1448.
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