A Primal Neural Network for Online Equality-Constrained Quadratic Programming
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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.
KeywordsRecurrent neural networks Online equality-constrained quadratic programming Global exponential convergence Robustness analysis
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.
Compliance with Ethical Standards
Conflict of interests
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
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