Abstract
A new recurrent neural network is presented to solve a general quadratic programming problem in real time. In contrast with the available neural networks, the new neural network is with fewer neurons for solving quadratic programming problems. The global convergence of the model is proven with contraction analysis. The discrete time model and an alternative model for solving the problem under irredundant equality constraints are also studied. Simulation results demonstrate that the proposed recurrent neural networks are effective.
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Acknowledgements
The authors would like to acknowledge the support of Guangdong Science Foundation of China under Grant No. S2011010006116 and No. 2015A030313587, Shenzhen Science Technology Project No. JCYJ20150417094158025, No. JCY20160307100530069 and GRCK20170424095924228,Shenzhen Institute of Information Technology Scientific Research Platform Cultivation Project (PT201704).
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Chen, S., Han, X., Tang, F., Lin, G. (2018). A New Recurrent Neural Network with Fewer Neurons for Quadratic Programming Problems. In: Li, K., Li, W., Chen, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2017. Communications in Computer and Information Science, vol 873. Springer, Singapore. https://doi.org/10.1007/978-981-13-1648-7_1
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DOI: https://doi.org/10.1007/978-981-13-1648-7_1
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