A mixture varying-gain dynamic learning network for solving nonlinear and nonconvex constrained optimization problems

• Published in: Neurocomputing (Amsterdam) Vol. 456; pp. 232 - 242
• Main Authors: Lu, Rongxiu, Qiu, Guanhua, Zhang, Zhijun, Deng, Xianzhi, Yang, Hui, Zhu, Zhenmin, Zhu, Jianyong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 07.10.2021
• Subjects: Inequality constraints; Nonlinear and nonconvex optimization; Recurrent neural networks; Inequality constraints; Recurrent neural networks; Nonlinear and nonconvex optimization
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2021.05.037

Nonlinear and nonconvex optimization problem (NNOP) is a challenging problem in control theory and applications. In this paper, a novel mixture varying-gain dynamic learning network (MVG-DLN) is proposed to solve NNOP with inequality constraints. To do so, first, this NNOP is transformed into some equations through Karush–Kuhn–Tucker (KKT) conditions and projection theorem, and the neuro-dynamics function can be obtained. Second, the time varying convergence parameter is utilized to obtain a faster convergence speed. Third, an integral term is used to strengthen the robustness. Theoretical analysis proves that the proposed MVG-DLN has global convergence and good robustness. Three numerical simulation comparisons between FT-FP-CDNN and MVG-DLN substantiate the faster convergence performance and greater robustness of the MVG-DLN in solving the nonlinear and nonconvex optimization problems.