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An Improved Recurrent Network for Online Equality-Constrained Quadratic Programming

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Advances in Brain Inspired Cognitive Systems (BICS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10023))

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Abstract

Encouraged by the success of conventional GradientNet and recently-proposed ZhangNet for online equality-constrained quadratic programming problem, an improved recurrent network and its electronic implementation are firstly proposed and developed in this paper. Exploited in the primal form of quadratic programming with linear equality constraints, the proposed neural model can solve the problem effectively. Moreover, compared to the existing recurrent networks, i.e., GradientNet (GN) and ZhangNet (ZN), our model can theoretically guarantee superior global exponential convergence performance. 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 for online equality-constrained quadratic programming.

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Acknowledgements

This work was funded by the Academy of Finland under No. 267581 and 298700.

Author information

Authors and Affiliations

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

    Ke Chen

  2. CAS Center for Excellence in Brain Science and Intelligence Technology, CASIA, Beijing, China

    Zhaoxiang Zhang

Authors
  1. Ke Chen
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  2. Zhaoxiang Zhang
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Corresponding author

Correspondence to Zhaoxiang Zhang .

Editor information

Editors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

  2. Computing Science and Mathematics, University of Stirling, Stirling, United Kingdom

    Amir Hussain

  3. Anhui University, Anhui, China

    Bin Luo

  4. National University of Singapore, Singapore, Singapore

    Kay Chen Tan

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Yi Zeng

  6. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zhaoxiang Zhang

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© 2016 Springer International Publishing AG

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Chen, K., Zhang, Z. (2016). An Improved Recurrent Network for Online Equality-Constrained Quadratic Programming. In: Liu, CL., Hussain, A., Luo, B., Tan, K., Zeng, Y., Zhang, Z. (eds) Advances in Brain Inspired Cognitive Systems. BICS 2016. Lecture Notes in Computer Science(), vol 10023. Springer, Cham. https://doi.org/10.1007/978-3-319-49685-6_1

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  • DOI: https://doi.org/10.1007/978-3-319-49685-6_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49684-9

  • Online ISBN: 978-3-319-49685-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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