Self-Organizing Robust Fuzzy Neural Network for Nonlinear System Modeling

• Published in: IEEE transaction on neural networks and learning systems Vol. 36; no. 1; pp. 911 - 923
• Main Authors: Han, Honggui, Wang, Jiaqian, Liu, Zheng, Yang, Hongyan, Qiao, Junfei
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2025
• Subjects: Analytical models; Dynamic learning algorithm based on α-divergence loss function (α-DLA); Fuzzy neural networks; Heuristic algorithms; information integration mechanism (IIM); Neurons; nonlinear system modeling; Prediction algorithms; Robustness; self-organization; Simulation
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2023.3334150

Fuzzy neural network (FNN) is a structured learning technique that has been successfully adopted in nonlinear system modeling. However, since there exist uncertain external disturbances arising from mismatched model errors, sensor noises, or unknown environments, FNN generally fails to achieve the desirable performance of modeling results. To overcome this problem, a self-organization robust FNN (SOR-FNN) is developed in this article. First, an information integration mechanism (IIM), consisting of partition information and individual information, is introduced to dynamically adjust the structure of SOR-FNN. The proposed mechanism can make itself adapt to uncertain environments. Second, a dynamic learning algorithm based on the <inline-formula> <tex-math notation="LaTeX">\alpha </tex-math></inline-formula>-divergence loss function (<inline-formula> <tex-math notation="LaTeX">\alpha </tex-math></inline-formula>-DLA) is designed to update the parameters of SOR-FNN. Then, this learning algorithm is able to reduce the sensibility of disturbances and improve the robustness of Third, the convergence of SOR-FNN is given by the Lyapunov theorem. Then, the theoretical analysis can ensure the successful application of SOR-FNN. Finally, the proposed SOR-FNN is tested on several benchmark datasets and a practical application to validate its merits. The experimental results indicate that the proposed SOR-FNN can obtain superior performance in terms of model accuracy and robustness.