Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/71584
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dc.titleRecursive percentage based hybrid pattern (RPHP) training for curve fitting
dc.contributor.authorUei, G.S.
dc.contributor.authorRamanathan, K.
dc.date.accessioned2014-06-19T03:25:25Z
dc.date.available2014-06-19T03:25:25Z
dc.date.issued2004
dc.identifier.citationUei, G.S.,Ramanathan, K. (2004). Recursive percentage based hybrid pattern (RPHP) training for curve fitting. 2004 IEEE Conference on Cybernetics and Intelligent Systems : 445-450. ScholarBank@NUS Repository.
dc.identifier.isbn0780386442
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/71584
dc.description.abstractIn this paper, we present the RPHP training algorithm, which finds several good local optimal points (pseudo global optima) automatically using an efficient combination of global and local search algorithms. This overcomes the problem of supervised learning algorithms being trapped in a local optima. Further, to solve a test pattern, we use a modified version of the Kth nearest neighbor (KNN) algorithm as a second level pattern distributor. We tested our approach on three curve fitting problems, whose coefficients were estimated both using genetic algorithms and the RPHP algorithm. The problems were chosen such that they had a small probability of finding a global optimal solution. It was found that the RPHP algorithms performed faster and improved generalization accuracy by as much as 25%.
dc.sourceScopus
dc.subjectGenetic algorithms
dc.subjectHybrid learning
dc.subjectPattern Learning
dc.subjectPercentage based training
dc.subjectTask decomposition
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.sourcetitle2004 IEEE Conference on Cybernetics and Intelligent Systems
dc.description.page445-450
dc.identifier.isiutNOT_IN_WOS
Appears in Collections:Staff Publications

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