A mixture varying-gain dynamic learning network for solving nonlinear and nonconvex constrained optimization problems
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 e...
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| Published in | Neurocomputing (Amsterdam) Vol. 456; pp. 232 - 242 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
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Elsevier B.V
07.10.2021
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| Subjects | |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Zhu, Zhenmin Deng, Xianzhi Zhu, Jianyong Lu, Rongxiu Zhang, Zhijun Qiu, Guanhua Yang, Hui |
| Author_xml | – sequence: 1 givenname: Rongxiu surname: Lu fullname: Lu, Rongxiu organization: School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330052, China – sequence: 2 givenname: Guanhua surname: Qiu fullname: Qiu, Guanhua organization: School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330052, China – sequence: 3 givenname: Zhijun surname: Zhang fullname: Zhang, Zhijun email: auzjzhang@scut.edu.cn organization: School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330052, China – sequence: 4 givenname: Xianzhi surname: Deng fullname: Deng, Xianzhi email: Audxz19971022@scut.edu.cn organization: School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China – sequence: 5 givenname: Hui surname: Yang fullname: Yang, Hui organization: School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330052, China – sequence: 6 givenname: Zhenmin surname: Zhu fullname: Zhu, Zhenmin organization: School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330052, China – sequence: 7 givenname: Jianyong surname: Zhu fullname: Zhu, Jianyong organization: School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330052, China |
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| Snippet | Nonlinear and nonconvex optimization problem (NNOP) is a challenging problem in control theory and applications. In this paper, a novel mixture varying-gain... |
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| SubjectTerms | Inequality constraints Nonlinear and nonconvex optimization Recurrent neural networks |
| Title | A mixture varying-gain dynamic learning network for solving nonlinear and nonconvex constrained optimization problems |
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