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Learning fuzzy systems: an ojective function-approach
dc.contributor.author | Höppner, Frank |
dc.contributor.author | Klawonn, Frank |
dc.date.accessioned | 2007-10-05T09:24:09Z |
dc.date.available | 2007-10-05T09:24:09Z |
dc.date.issued | 2004 |
dc.identifier.uri | http://hdl.handle.net/2099/3643 |
dc.description.abstract | One of the most important aspects of fuzzy systems is that they are easily understandable and interpretable. This property, however, does not come for free but poses some essential constraints on the parameters of a fuzzy system (like the linguistic terms), which are sometimes overlooked when learning fuzzy system automatically from data. In this paper, an objective function-based approach to learn fuzzy systems is developed, taking these constraints explicitly into account. Starting from fuzzy c-means clustering, several modifications of the basic algorithm are proposed, affecting the shape of the membership functions, the partition of individual variables and the coupling of input space partitioning and local function approximation. |
dc.format.extent | 143-162 |
dc.language.iso | eng |
dc.publisher | Universitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica |
dc.relation.ispartof | Mathware & soft computing . 2004 Vol. 11 Núm. 3 |
dc.rights | Reconeixement-NoComercial-CompartirIgual 3.0 Espanya |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.title | Learning fuzzy systems: an ojective function-approach |
dc.type | Article |
dc.subject.lemac | Intel·ligència artificial |
dc.subject.lemac | Cluster, Anàlisi de |
dc.subject.ams | Classificació AMS::68 Computer science::68T Artificial intelligence |
dc.rights.access | Open Access |
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