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

作者：Lu, Rongxiu; Qiu, Guanhua; Zhang, Zhijun^*; Deng, Xianzhi; Yang, Hui; Zhu, Zhenmin; Zhu, Jianyong

来源：NEUROCOMPUTING, 2021, 456: 232-242.

DOI：10.1016/j.neucom.2021.05.037

• Recurrent neural networks
• Nonlinear and nonconvex optimization
• Inequality constraints

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 neurodynamics 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.