An Efficient Second-Order Algorithm for Self-Organizing Fuzzy Neural Networks

Intelligent computing technologies are useful and important for online data modeling, where system dynamics may be nonstationary with some uncertainties. In this paper, an efficient learning mechanism is developed for building self-organizing fuzzy neural networks (SOFNNs), where a second-order algo...

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Vydané v:	IEEE transactions on cybernetics Ročník 49; číslo 1; s. 14 - 26
Hlavní autori:	Han, Honggui, Zhang, Lu, Wu, Xiaolong, Qiao, Junfei
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.01.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Adaptive algorithms Adaptive learning rate strategy Algorithm design and analysis Approximation algorithms Artificial neural networks Computing time Convergence Data models Fuzzy logic Fuzzy neural networks Machine learning Mathematical models Model accuracy Neural networks Neurons Nonlinear systems nonlinear systems modeling On-line systems Process parameters second-order algorithm self-organizing fuzzy neural networks (SOFNNs) Semiconductor optical amplifiers System dynamics
ISSN:	2168-2267, 2168-2275, 2168-2275
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