The Min-Max Function Differentiation and Training of Fuzzy Neural Networks  

Oleh:

Zhang, Xinghu ; Hang, Chang-Chieh ; Tan, Shaohua ; Wang, Pei-Zhuang

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 7 no. 5 (1996), page 1139-1150.
Topik: fuzzy neural networks; min - max function; training; fuzzy neural networks

Ketersediaan

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

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Isi artikel

This paper discusses the ? - rule and training of min - max neural networks by developing a differentiation theory for min - max functions, the functions containing min (?) and / or max (V) operations. We first prove that under certain conditions all min - max functions are continuously differentiable almost everywhere in the real number field & Rfr; and derive the explicit formulas for the differentiation. These results are the basis for developing the ? - rule for the training of min - max neural networks. The convergence of the new ? - rule is proved theoretically using the stochastic theory, and is demonstrated with a simulation example.

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