A design of RFTOG model for distributed real-time applications

Kang, Myungseok; Kim, Hagbae

doi:10.1007/s10845-008-0215-0

Cited by 2 publications

(1 citation statement)

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“…It aims to a regularization of the network architecture based on 2 Advances in Fuzzy Systems learning theory and hyperplane clustering-based techniques [4][5][6][7]. The underlying idea of such techniques received in the literature many acknowledgments as, for example, in the case of theoretical models [8][9][10][11][12][13][14] as well as applications to specific fields [15][16][17][18][19][20][21][22][23][24][25][26].…”

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confidence: 99%

A Hierarchical Procedure for the Synthesis of ANFIS Networks

Panella

2012

Advances in Fuzzy Systems

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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.

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