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A Primal Neural Network for Online Equality-Constrained Quadratic Programming

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Abstract

This paper aims at solving online equality-constrained quadratic programming problem, which is widely encountered in science and engineering, e.g., computer vision and pattern recognition, digital signal processing, and robotics. Recurrent neural networks such as conventional GradientNet and ZhangNet are considered as powerful solvers for such a problem in light of its high computational efficiency and capability of circuit realisation. In this paper, an improved primal recurrent neural network and its electronic implementation are proposed and analysed. Compared to the existing recurrent networks, i.e. GradientNet and ZhangNet, our network can theoretically guarantee superior global exponential convergence. Robustness performance of our such neural model is also analysed under a large model implementation error, with the upper bound of stead-state solution error estimated. Simulation results demonstrate theoretical analysis on the proposed model, which also verify the effectiveness of the proposed model for online equality-constrained quadratic programming.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant 61773375, Grant 61375036, and Grant 61511130079, and in part by the Microsoft Collaborative Research Project, and by the Academy of Finland under No.298700.

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Authors and Affiliations

  1. Lab of Signal Processing, Tampere University of Technology, Tampere, Finland

    Ke Chen

  2. CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Beijing, China

    Zhaoxiang Zhang

  3. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (NLPR, CASIA), Beijing, China

    Zhaoxiang Zhang

  4. University of Chinese Academy of Sciences (UCAS), Beijing, China

    Zhaoxiang Zhang

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  1. Ke Chen
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  2. Zhaoxiang Zhang
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Correspondence to Zhaoxiang Zhang.

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Chen, K., Zhang, Z. A Primal Neural Network for Online Equality-Constrained Quadratic Programming. Cogn Comput 10, 381–388 (2018). https://doi.org/10.1007/s12559-017-9510-4

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  • DOI: https://doi.org/10.1007/s12559-017-9510-4

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