ZNN for solving online time-varying linear matrix–vector inequality via equality conversion

Dongsheng Guo and Yunong Zhang

Applied Mathematics and Computation, 2015, vol. 259, issue C, 327-338

Abstract: In this paper, a special recurrent neural network termed Zhang neural network (ZNN) is proposed and investigated for solving online time-varying linear matrix–vector inequality (LMVI) via equality conversion. That is, by introducing a time-varying vector (of which each element is great than or equal to zero), such a time-varying linear inequality can be converted to a time-varying matrix–vector equation. Then, the ZNN model is developed and investigated for solving online the time-varying matrix–vector equation (as well as the time-varying LMVI) by employing the ZNN design formula. The resultant ZNN model exploits the time-derivative information of time-varying coefficients. Computer-simulation results further demonstrate the efficacy and superiority of the proposed ZNN model for solving online the time-varying LMVI (and the converted time-varying matrix–vector equation).

Keywords: Zhang neural network (ZNN); Time-varying; Linear matrix–vector inequality (LMVI); Conversion; ZNN design formula (search for similar items in EconPapers)
Date: 2015
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:259:y:2015:i:c:p:327-338

DOI: 10.1016/j.amc.2015.02.060

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