An ART-Based Construction of RBF Networks  

Oleh:

Lee, Shie-Jue ; Hou, Chun-Liang

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 13 no. 6 (2002), page 1308-1321.
Topik: networks; ART - based construction; RBF networks

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.7A Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

Radial basis function (RBF) networks are widely used for modeling a function from given input - output patterns. However, two difficulties are involved with traditional RBF (TRBF) networks : The initial configuration of an RBF network needs to be determined by a trial - and - error method, and the performance suffers when the desired output has abrupt changes or constant values in certain intervals. We propose a novel approach to over. come these difficulties. New kernel functions are used for hidden nodes, and the number of nodes is determined automatically by an adaptive resonance theory (ART) - like algorithm. Parameters and weights are initialized appropriately, and then tuned and adjusted by the gradient - descent method to improve the performance of the network. Experimental results have shown that the RBF networks constructed by our method have a smaller number of nodes, a faster learning speed, and a smaller approximation error than the networks produced by other methods.

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