A New One-layer Recurrent Neural Network for Solving Nonsmooth Pseudoconvex Optimization Problems

Xin YU; Huixia LU; Lingzhen WU; Liuming XU

doi:10.11999/JEIT200558

Volume 43 Issue 8

Aug. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(8): 2421-2429

Xin YU, Huixia LU, Lingzhen WU, Liuming XU. A New One-layer Recurrent Neural Network for Solving Nonsmooth Pseudoconvex Optimization Problems[J]. Journal of Electronics & Information Technology, 2021, 43(8): 2421-2429. doi: 10.11999/JEIT200558

Citation:

Xin YU, Huixia LU, Lingzhen WU, Liuming XU. A New One-layer Recurrent Neural Network for Solving Nonsmooth Pseudoconvex Optimization Problems[J]. Journal of Electronics & Information Technology, 2021, 43(8): 2421-2429. doi: 10.11999/JEIT200558

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A New One-layer Recurrent Neural Network for Solving Nonsmooth Pseudoconvex Optimization Problems

doi: 10.11999/JEIT200558

Xin YU^{1, 3},
Huixia LU¹,
Lingzhen WU^{2
,
,},
Liuming XU¹

1.
Department of Computer and Electronic Information, Guangxi University, Nanning 530004, China
2.
School of Computer Science and Engineering, Guilin University of Aerospace Technology, Guilin 541004, China
3.
Guangxi Key Laboratory of Multimedia Communications and Network Technology, Nanning 530004, China

Funds: The National Natural Science Foundation of China (61862004, 61462006)

Received Date: 2020-07-07
Rev Recd Date: 2020-12-06

Available Online: 2020-12-16

Publish Date: 2021-08-10

Abstract

Abstract

Pseudoconvex optimization problems are a special kind of nonconvex optimization problems, which often appear in various scientific and engineering applications, so they have great research value. Considering the shortcomings of the existing neural network model to solve the nonsmooth pseudoconvex optimization problem, a new single-layer recurrent neural network model based on differential inclusion theory is proposed. Through theoretical analysis, it is proved that the state solution of the neural network converges to the feasible region within a limited time and stays in it forever. Finally, the state solution of the neural network converges to the optimal solution of the original optimization problem. At the end of the article, the validity of the proposed theory is verified through numerical experiments. Compared with existing neural networks, the neural network model proposed in this paper is simple in structure, does not need to calculate penalty parameters in advance, and has no special requirements for the selection of initial points.
- Neural network,
- Nonsmooth pseudoconvex optimization,
- Convergence,
- Optimal solution

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References(22)

References

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