A Primal Neural Network for Online Equality-Constrained Quadratic Programming
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Details
| Original language | English |
|---|---|
| Pages (from-to) | 381–388 |
| Number of pages | 8 |
| Journal | Cognitive Computation |
| Volume | 10 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2018 |
| Publication type | A1 Journal article-refereed |
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.
ASJC Scopus subject areas
Keywords
- Global exponential convergence, Online equality-constrained quadratic programming, Recurrent neural networks, Robustness analysis