Adaptive Neuro-Fuzzy Inference Systems (ANFIS) are one of the most popular type of fuzzy neural networks. An usual approach to the synthesis of ANFIS networks is based on clustering a training set of numerical examples of the unknown mapping to be approximated, Several different clustering procedures can be adopted for this purpose, but most of them are affected by serious drawbacks. In the present paper, we propose a novel clustering approach in order to overcome these problems. It determines directly the consequent part of ANFIS rules; successively, the fuzzy antecedent part of each rule is determined by using a Min-Max classifier. The resulting ANFIS architecture is optimized by means of a constructive procedure, which we further propose in this paper. It allows to determine automatically the optimal number of rules by applying well-known results of learning theory. Simulation tests and comparison with other techniques are discussed in order to prove the validity of the proposed approach.

ANFIS synthesis by hyperplane clustering / Panella, Massimo; Rizzi, Antonello; FRATTALE MASCIOLI, Fabio Massimo; Martinelli, Giuseppe. - STAMPA. - 1:(2001), pp. 340-345. ((Intervento presentato al convegno 9th International-Fuzzy-Systems-Association World Congress/20th North-American-Fuzzy-Information-Processing-Society, International Conference tenutosi a VANCOUVER, CANADA nel JUL 25-28, 2001 [10.1109/nafips.2001.944275].

ANFIS synthesis by hyperplane clustering

PANELLA, Massimo;RIZZI, Antonello;FRATTALE MASCIOLI, Fabio Massimo;MARTINELLI, Giuseppe
2001

Abstract

Adaptive Neuro-Fuzzy Inference Systems (ANFIS) are one of the most popular type of fuzzy neural networks. An usual approach to the synthesis of ANFIS networks is based on clustering a training set of numerical examples of the unknown mapping to be approximated, Several different clustering procedures can be adopted for this purpose, but most of them are affected by serious drawbacks. In the present paper, we propose a novel clustering approach in order to overcome these problems. It determines directly the consequent part of ANFIS rules; successively, the fuzzy antecedent part of each rule is determined by using a Min-Max classifier. The resulting ANFIS architecture is optimized by means of a constructive procedure, which we further propose in this paper. It allows to determine automatically the optimal number of rules by applying well-known results of learning theory. Simulation tests and comparison with other techniques are discussed in order to prove the validity of the proposed approach.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/249775
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