Fuzzy Nonlinear Regression With Fuzzified Radial Basis Function Network  

Oleh:

Dong Zhang ; Luo-Feng Deng ; Kai-Yuan Cai ; So, Albert

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Fuzzy Systems vol. 13 no. 6 (Dec. 2005), page 742-760.
Topik: Fuzzified Radial Basis Function Network (FRBFN); Fuzzy Neural Network; Fuzzy Number; Fuzzy Regression; Universal Approximation

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II70 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

A fuzzified radial basis function network (FRBFN) is a kind of fuzzy neural network that is obtained by direct fuzzification of the well known neural model RBFN. A FRBFN contains fuzzy weights and can handle fuzzy-in fuzzy-out data. This paper shows that a FRBFN can also be interpreted as a kind of fuzzy expert system. Hence it owns the advantages of simple structure and clear physical meaning. Some metrics for fuzzy numbers have been extended to the metrics for n-dimensional fuzzy vectors, which are applicable to computations in FRBFNs. The corresponding metric spaces for n-dimensional fuzzy vectors are proved to be complete. Further, FRBFNs are proved to be able to act as universal function approximators for any continuous fuzzy function defined on a compact set. This paper applies the proposed FRBFN to nonparametric fuzzy nonlinear regression problems for multidimensional LR-type fuzzy data. Fuzzy nonlinear regression with FRBFNs can be formulated as a nonlinear mathematical programming problem. Two training algorithms are proposed to quickly solve the two types of problems under different criteria and constraint conditions, namely, the two-stage and BP (Back-Propagation) training algorithms. Simulation studies are carried out to verify the feasibility and demonstrate the advantages of the proposed approaches.

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