A Hierarchical Procedure for the Synthesis of ANFIS Networks
Adaptive neurofuzzy inference systems (ANFIS) represent an efficient technique for the solution of function approximation problems. When numerical samples are available in this regard, the synthesis of ANFIS networks can be carried out exploiting clustering algorithms. Starting from a hyperplane clu...
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | Advances in Fuzzy Systems |
| Online Access: | http://dx.doi.org/10.1155/2012/491237 |
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| _version_ | 1832550267730526208 |
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| author | Massimo Panella |
| author_facet | Massimo Panella |
| author_sort | Massimo Panella |
| collection | DOAJ |
| description | Adaptive neurofuzzy inference systems (ANFIS) represent an efficient technique for the solution of function approximation problems. When numerical samples are available in this regard, the synthesis of ANFIS networks can be carried out exploiting clustering algorithms. Starting from a hyperplane clustering synthesis in the joint input-output space, a computationally efficient optimization of ANFIS networks is proposed in this paper. It is based on a hierarchical constructive procedure, by which the number of rules is progressively increased and the optimal one is automatically determined on the basis of learning theory in order to maximize the generalization capability of the resulting ANFIS network. Extensive computer simulations prove the validity of the proposed algorithm and show a favorable comparison with other well-established techniques. |
| format | Article |
| id | doaj-art-3eb51415649740619eb1db9c18347ff9 |
| institution | Kabale University |
| issn | 1687-7101 1687-711X |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Fuzzy Systems |
| spelling | doaj-art-3eb51415649740619eb1db9c18347ff92025-02-03T06:07:07ZengWileyAdvances in Fuzzy Systems1687-71011687-711X2012-01-01201210.1155/2012/491237491237A Hierarchical Procedure for the Synthesis of ANFIS NetworksMassimo Panella0Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, via Eudossiana 18, 00184 Rome, ItalyAdaptive neurofuzzy inference systems (ANFIS) represent an efficient technique for the solution of function approximation problems. When numerical samples are available in this regard, the synthesis of ANFIS networks can be carried out exploiting clustering algorithms. Starting from a hyperplane clustering synthesis in the joint input-output space, a computationally efficient optimization of ANFIS networks is proposed in this paper. It is based on a hierarchical constructive procedure, by which the number of rules is progressively increased and the optimal one is automatically determined on the basis of learning theory in order to maximize the generalization capability of the resulting ANFIS network. Extensive computer simulations prove the validity of the proposed algorithm and show a favorable comparison with other well-established techniques.http://dx.doi.org/10.1155/2012/491237 |
| spellingShingle | Massimo Panella A Hierarchical Procedure for the Synthesis of ANFIS Networks Advances in Fuzzy Systems |
| title | A Hierarchical Procedure for the Synthesis of ANFIS Networks |
| title_full | A Hierarchical Procedure for the Synthesis of ANFIS Networks |
| title_fullStr | A Hierarchical Procedure for the Synthesis of ANFIS Networks |
| title_full_unstemmed | A Hierarchical Procedure for the Synthesis of ANFIS Networks |
| title_short | A Hierarchical Procedure for the Synthesis of ANFIS Networks |
| title_sort | hierarchical procedure for the synthesis of anfis networks |
| url | http://dx.doi.org/10.1155/2012/491237 |
| work_keys_str_mv | AT massimopanella ahierarchicalprocedureforthesynthesisofanfisnetworks AT massimopanella hierarchicalprocedureforthesynthesisofanfisnetworks |