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• W1964693520 startingPage "10066" @default.
• W1964693520 abstract "In this paper, a new complex-valued recurrent neural network (CVRNN) called complex-valued Zhang neural network (CVZNN) is proposed and simulated to solve the complex-valued time-varying matrix-inversion problems. Such a CVZNN model is designed based on a matrix-valued error function in the complex domain, and utilizes the complex-valued first-order time-derivative information of the complex-valued time-varying matrix for online inversion. Superior to the conventional complex-valued gradient-based neural network (CVGNN) and its related methods, the state matrix of the resultant CVZNN model can globally exponentially converge to the theoretical inverse of the complex-valued time-varying matrix in an error-free manner. Moreover, by exploiting the design parameter γ>1, superior convergence can be achieved for the CVZNN model to solve such complex-valued time-varying matrix inversion problems, as compared with the situation without design parameter γ involved (i.e., the situation with γ=1). Computer-simulation results substantiate the theoretical analysis and further demonstrate the efficacy of such a CVZNN model for online complex-valued time-varying matrix inversion." @default.
• W1964693520 created "2016-06-24" @default.
• W1964693520 creator A5002078531 @default.
• W1964693520 creator A5019886822 @default.
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• W1964693520 date "2011-08-01" @default.
• W1964693520 modified "2023-10-08" @default.
• W1964693520 title "Complex-valued Zhang neural network for online complex-valued time-varying matrix inversion" @default.
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• W1964693520 doi "https://doi.org/10.1016/j.amc.2011.04.085" @default.
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