Compensatory Neurofuzzy Systems With Fast Learning Algorithms  

Oleh:

Kandel, A. ; Zhang, Yan-Qing

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 9 no. 1 (1998), page 83-105.
Topik: Neuro fuzzy; compensatory; neurofuzzy systems; fast learning; algorithms

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.3 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

In this paper, a new adaptive fuzzy reasoning method using compensatory fuzzy operators is proposed to make a fuzzy logic system more adaptive and more effective. Such a compensatory fuzzy logic system is proved to be a universal approximator. The compensatory neural fuzzy networks built by both control - oriented fuzzy neurons and decision - oriented fuzzy neurons cannot only adaptively adjust fuzzy membership functions but also dynamically optimize the adaptive fuzzy reasoning by using a compensatory learning algorithm. The simulation results of a cart - pole balancing system and nonlinear system modeling have shown that : 1) the compensatory neurofuzzy system can effectively learn commonly used fuzzy IF - THEN rules from either well - defined initial data or ill - defined data ; 2) the convergence speed of the compensatory learning algorithm is faster than that of the conventional backpropagation algorithm ; and 3) the efficiency of the compensatory learning algorithm can be improved by choosing an appropriate compensatory degree.

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