Hybrid GMDH-type modeling for nonlinear systems: Synergism to intelligent identification
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, D. | - |
dc.contributor.author | Seo, S.-J. | - |
dc.contributor.author | Park, G.-T. | - |
dc.date.accessioned | 2021-09-09T00:30:40Z | - |
dc.date.available | 2021-09-09T00:30:40Z | - |
dc.date.created | 2021-06-17 | - |
dc.date.issued | 2009 | - |
dc.identifier.issn | 0965-9978 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/121920 | - |
dc.description.abstract | This paper presents a novel hybrid GMDH-type algorithm which combines neural networks (NNs) with an approximation scheme (self-organizing polynomial neural network: SOPNN). This composite structure is developed to establish a new heuristic approximation method for identification of nonlinear static systems. NNs have been widely employed to process modeling and control because of their approximation capabilities. And SOPNN is an analysis technique for identifying nonlinear relationships between the inputs and outputs of such systems and builds hierarchical polynomial regressions of required complexity. Therefore, the combined model can harmonize NNs with SOPNN and find a workable synergistic environment. Simulation results of the nonlinear static system are provided to show that the proposed method is much more accurate than other modeling methods. Thus, it can be considered for efficient system identification methodology. © 2009 Elsevier Ltd. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | Elsevier Ltd | - |
dc.subject | Approximation algorithms | - |
dc.subject | Heuristic algorithms | - |
dc.subject | Heuristic methods | - |
dc.subject | Hierarchical systems | - |
dc.subject | Hybrid systems | - |
dc.subject | Nonlinear systems | - |
dc.subject | Polynomial approximation | - |
dc.subject | Structure (composition) | - |
dc.subject | Analysis techniques | - |
dc.subject | Approximation capabilities | - |
dc.subject | Approximation schemes | - |
dc.subject | Combined models | - |
dc.subject | Efficient systems | - |
dc.subject | Heuristic approximation | - |
dc.subject | Hybrid GMDH-type algorithm | - |
dc.subject | Intelligent identifications | - |
dc.subject | Modeling methods | - |
dc.subject | Non-linear relationships | - |
dc.subject | Non-linear statics | - |
dc.subject | Polynomial neural networks | - |
dc.subject | Polynomial regressions | - |
dc.subject | Process modeling and controls | - |
dc.subject | Self-organizing | - |
dc.subject | Simulation results | - |
dc.subject | SOPNN | - |
dc.subject | System identification | - |
dc.subject | Neural networks | - |
dc.title | Hybrid GMDH-type modeling for nonlinear systems: Synergism to intelligent identification | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Park, G.-T. | - |
dc.identifier.doi | 10.1016/j.advengsoft.2009.01.029 | - |
dc.identifier.scopusid | 2-s2.0-65549157421 | - |
dc.identifier.bibliographicCitation | Advances in Engineering Software, v.40, no.10, pp.1087 - 1094 | - |
dc.relation.isPartOf | Advances in Engineering Software | - |
dc.citation.title | Advances in Engineering Software | - |
dc.citation.volume | 40 | - |
dc.citation.number | 10 | - |
dc.citation.startPage | 1087 | - |
dc.citation.endPage | 1094 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Approximation algorithms | - |
dc.subject.keywordPlus | Heuristic algorithms | - |
dc.subject.keywordPlus | Heuristic methods | - |
dc.subject.keywordPlus | Hierarchical systems | - |
dc.subject.keywordPlus | Hybrid systems | - |
dc.subject.keywordPlus | Nonlinear systems | - |
dc.subject.keywordPlus | Polynomial approximation | - |
dc.subject.keywordPlus | Structure (composition) | - |
dc.subject.keywordPlus | Analysis techniques | - |
dc.subject.keywordPlus | Approximation capabilities | - |
dc.subject.keywordPlus | Approximation schemes | - |
dc.subject.keywordPlus | Combined models | - |
dc.subject.keywordPlus | Efficient systems | - |
dc.subject.keywordPlus | Heuristic approximation | - |
dc.subject.keywordPlus | Hybrid GMDH-type algorithm | - |
dc.subject.keywordPlus | Intelligent identifications | - |
dc.subject.keywordPlus | Modeling methods | - |
dc.subject.keywordPlus | Non-linear relationships | - |
dc.subject.keywordPlus | Non-linear statics | - |
dc.subject.keywordPlus | Polynomial neural networks | - |
dc.subject.keywordPlus | Polynomial regressions | - |
dc.subject.keywordPlus | Process modeling and controls | - |
dc.subject.keywordPlus | Self-organizing | - |
dc.subject.keywordPlus | Simulation results | - |
dc.subject.keywordPlus | SOPNN | - |
dc.subject.keywordPlus | System identification | - |
dc.subject.keywordPlus | Neural networks | - |
dc.subject.keywordAuthor | Heuristic approximation | - |
dc.subject.keywordAuthor | Hybrid GMDH-type algorithm | - |
dc.subject.keywordAuthor | Neural networks | - |
dc.subject.keywordAuthor | SOPNN | - |
dc.subject.keywordAuthor | System identification | - |
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