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Noise-suppressing zeroing neural network for online solving time-varying nonlinear optimization problem: a control-based approach

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Abstract

Time-varying nonlinear optimization problems with different noises often arise in the fields of scientific and engineering research. Noises are unavoidable in the practical workspace, but the most existing models for time-varying nonlinear optimization problems carry out with one assume that the computing process is free of noises. In this paper, from a control-theoretical framework, noise-suppressing zeroing neural dynamic (NSZND) model is developed, analyzed and investigated by feat of continuous-time zeroing neural network model, which behaves efficiently for hurdling online time-varying nonlinear optimization problems with the presence of different noises. Further, for speeding the rate of convergence, general noise-suppressing zeroing neural network (GNSZNN) model with different activation functions is discussed. Then, theoretical analyses show that the proposed noise-suppressing zeroing neural network model derived from NSZND model has the global convergence property in the presence of different kinds of noises. Besides, how GNSZNN model performs with different activation functions is also proved in detail. In addition, numerical results are provided to substantiate the feasibility and superiority of GNSZNN model for online time-varying nonlinear optimization problems with inherent tolerance to noises.

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Funding

The work is supported in part by the National Natural Science Foundation of China under Grants 61873304, 11701209 and 51875047, and also in part by the China Postdoctoral Science Foundation Funded Project under Grant 2018M641784, 2019T120240 and also in part by the Key Science and Technology Projects of Jilin Province, China, Grant Nos. 20190302025GX, 20170204067GX, and 20180201105GX and also in part by the Industrial Innovation Special Funds Project of Jilin Province, China, Grant No. 2018C038-2 and also in part by the Jilin Engineering Laboratory for Intelligence Robot and Visual Measurement Technology, Grant No. 2019C010 and also in part by the Fundamental Research Funds for the Central Universities (No. lzujbky-2019-89).

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Authors and Affiliations

  1. Department of Control Engineering, Changchun University of Technology, Changchun, 130012, China

    Zhongbo Sun & Keping Liu

  2. Key Laboratory of Bionic Engineering of Ministry of Education, Jilin University, Changchun, 130025, China

    Zhongbo Sun

  3. College of Communication Engineering, Jilin University, Changchun, 130025, China

    Tian Shi

  4. School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China

    Lin Wei & Long Jin

  5. School of Information Technology, Jilin Agricultural University, Changchun, 130117, China

    Yingyi Sun

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  1. Zhongbo Sun
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  2. Tian Shi
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  3. Lin Wei
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Correspondence to Keping Liu.

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Sun, Z., Shi, T., Wei, L. et al. Noise-suppressing zeroing neural network for online solving time-varying nonlinear optimization problem: a control-based approach. Neural Comput & Applic 32, 11505–11520 (2020). https://doi.org/10.1007/s00521-019-04639-2

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