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

• Published in: IEEE transactions on cybernetics Vol. 49; no. 1; pp. 14 - 26
• Main Authors: Han, Honggui, Zhang, Lu, Wu, Xiaolong, Qiao, Junfei
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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
• DOI: 10.1109/TCYB.2017.2762521

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.