Efficient Learning Algorithms for Three-Layer Regular Feedforward Fuzzy Neural Networks  

Oleh:

Liu, Puyin ; Li, Hongxing

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 15 no. 3 (May 2004), page 545-558.
Topik: fuzzy neural networks; efficient; learning algorithms; three - layer regular; fuzzy neural networks

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.10 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

Lihat Detail Induk

Isi artikel

A key step of using gradient descend methods to develop learning algorithms of a regular feedforward fuzzy neural network (FNN) is to differentiate max - min functions, which contain max(?) and min(?) operations. The paper aims at several objectives. First, investigate further the differentiation of ? - ? functions. Second, employ general fuzzy numbers, which include triangular and trapezoidal fuzzy numbers as special cases to define a three - layer regular FNN. The general fuzzy numbers related can be approximately determined by their corresponding finite level sets. So, we can approximately represent the input - output (I/O) relationship of the regular FNN as functions of the endpoints of all finite level sets. Third, a fuzzy back - propagation algorithm is presented. And to speed up the convergence of the learning algorithm, a fuzzy conjugate gradient algorithm for fuzzy weights and biases is developed, furthermore, the convergence of the algorithm is analyzed, systematically. Finally, some real simulations demonstrate the efficiency of our learning algorithms. The regular FNN is applied to the approximate realization of fuzzy inference rules and fuzzy functions defined on given compact sets.

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