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dc.contributor.authorLin, Cheng-Jianen_US
dc.contributor.authorChen, Cheng-Hungen_US
dc.contributor.authorLee, Chi-Yungen_US
dc.date.accessioned2014-12-08T15:10:59Z-
dc.date.available2014-12-08T15:10:59Z-
dc.date.issued2008-09-01en_US
dc.identifier.issn1016-2364en_US
dc.identifier.urihttp://hdl.handle.net/11536/8409-
dc.description.abstractIn order to enhance the immune algorithm (IA) performance and find the optimal solution when dealing with difficult problems, we propose an efficient immune-based particle swarm optimization (IPSO) for use in TSK-type neuro-fuzzy networks for solving the identification and prediction problems. The proposed IPSO combines the immune algorithm (IA) and particle swarm optimization (PSO) to perform parameter learning. The IA uses the clonal selection principle, such that antibodies between others of high similar degree are affected, and these antibodies, after the process, will have higher quality, accelerating the search and increasing the global search capacity. The PSO algorithm has proved to be very effective for solving global optimization. It is not only a recently invented high-performance optimizer that is easy to understand and implement, but it also requires little computational bookkeeping and generally only a few lines of code. Hence, we employed the advantages of PSO to improve the mutation mechanism of immune algorithm. Experiments with synthetic and real data sets have performed in order to show the applicability of the proposed approach and also to compare with other methods in the literature.en_US
dc.language.isoen_USen_US
dc.subjectneuro-fuzzy networken_US
dc.subjectimmune system algorithmen_US
dc.subjectparticle swarm optimizationen_US
dc.subjectbackpropagationen_US
dc.subjectidentificationen_US
dc.subjectpredictionen_US
dc.titleEfficient immune-based particle swarm optimization learning for neuro-fuzzy networks designen_US
dc.typeArticleen_US
dc.identifier.journalJOURNAL OF INFORMATION SCIENCE AND ENGINEERINGen_US
dc.citation.volume24en_US
dc.citation.issue5en_US
dc.citation.spage1505en_US
dc.citation.epage1520en_US
dc.contributor.department電控工程研究所zh_TW
dc.contributor.departmentInstitute of Electrical and Control Engineeringen_US
dc.identifier.wosnumberWOS:000259662700014-
dc.citation.woscount4-
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