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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Vydáno v:IEEE transactions on cybernetics Ročník 49; číslo 1; s. 14 - 26
Hlavní autoři: Han, Honggui, Zhang, Lu, Wu, Xiaolong, Qiao, Junfei
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.01.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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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Shrnutí: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 algorithm (SOA) with adaptive learning rate is employed, the network size and the parameters can be determined simultaneously in the learning process. First, all parameters of SOFNN are adjusted by using the SOA strategy to achieve fast convergence through a powerful search scheme. Second, the structure of SOFNN can be self-organized using the relative importance index of each rule. The fuzzy rules used in SOFNN with SOA (SOA-SOFNN) are generated or pruned automatically to reduce the computational complexity and potentially improve the generalization power. Finally, a theoretical analysis on the learning convergence of the proposed SOA-SOFNN is given to show the computational efficiency. To demonstrate the merits of our proposed approach for data modeling, several benchmark datasets, and a real world application associated with nonlinear systems modeling problems are examined with comparisons against other existing methods. The results indicate that our proposed SOA-SOFNN performs favorably in terms of both learning speed and prediction accuracy for online data modeling.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2017.2762521