DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, J | - |
dc.contributor.author | Suga, Y | - |
dc.contributor.author | Won, S | - |
dc.date.accessioned | 2016-04-01T01:57:29Z | - |
dc.date.available | 2016-04-01T01:57:29Z | - |
dc.date.created | 2009-08-28 | - |
dc.date.issued | 2006-04 | - |
dc.identifier.issn | 1063-6706 | - |
dc.identifier.other | 2006-OAK-0000005848 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/24089 | - |
dc.description.abstract | This paper presents a new fuzzy inference system for modeling of nonlinear dynamic systems based on input and output data with measurement noise. The proposed fuzzy system has a number of fuzzy rules and parameter values of membership functions which are automatically generated using the extended relevance vector machine (RVM). The RVM has a probabilistic Bayesian learning framework and has good generalization capability. The RVM consists of the sum of product of weight and kernel function which projects input space into high dimensional feature space. The structure of proposed fuzzy system is same as that of the Takagi-Sugeno fuzzy model. However, in the proposed method, the number of fuzzy rules can be reduced under the process of optimizing a marginal likelihood by adjusting parameter values of kernel functions using the gradient ascent method. After a fuzzy system is determined, coefficients in consequent part are found by the least square method. Examples illustrate effectiveness of the proposed new fuzzy inference system. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGI | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON FUZZY SYSTEMS | - |
dc.subject | fuzzy inference system (FIS) | - |
dc.subject | kernel function | - |
dc.subject | nonlinear dynamic system with noise | - |
dc.subject | relevance vector machine | - |
dc.subject | INFERENCE SYSTEM | - |
dc.subject | NETWORK | - |
dc.subject | CLASSIFICATION | - |
dc.subject | CONSTRUCTION | - |
dc.subject | REGRESSION | - |
dc.subject | MACHINE | - |
dc.title | A new approach to fuzzy modeling of nonlinear dynamic systems with noise: Relevance vector learning mechanism | - |
dc.type | Article | - |
dc.contributor.college | 전자전기공학과 | - |
dc.identifier.doi | 10.1109/TFUZZ2005.86 | - |
dc.author.google | Kim, J | - |
dc.author.google | Suga, Y | - |
dc.author.google | Won, S | - |
dc.relation.volume | 14 | - |
dc.relation.issue | 2 | - |
dc.relation.startpage | 222 | - |
dc.relation.lastpage | 231 | - |
dc.contributor.id | 10083575 | - |
dc.relation.journal | IEEE TRANSACTIONS ON FUZZY SYSTEMS | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCI | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON FUZZY SYSTEMS, v.14, no.2, pp.222 - 231 | - |
dc.identifier.wosid | 000236806800006 | - |
dc.date.tcdate | 2019-01-01 | - |
dc.citation.endPage | 231 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 222 | - |
dc.citation.title | IEEE TRANSACTIONS ON FUZZY SYSTEMS | - |
dc.citation.volume | 14 | - |
dc.contributor.affiliatedAuthor | Won, S | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 23 | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | fuzzy inference system (FIS) | - |
dc.subject.keywordAuthor | kernel function | - |
dc.subject.keywordAuthor | nonlinear dynamic system with noise | - |
dc.subject.keywordAuthor | relevance vector machine | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
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