Convergence analysis of Zhang neural networks solving time-varying linear equations but without using time-derivative information

For online solution of time-varying linear equations, a special kind of recurrent neural networks has recently been proposed by Zhang et al. It has been proved that global exponential convergence of such recurrent neural networks (or termed Zhang neural networks, ZNN, for presentation convenience) can be achieved. For easier hardware-realization, as well as to find out the effect of time-derivative terms on global exponential convergence, the ZNN model with no time-derivative information is investigated, analyzed and simulated in this paper. Theoretical analysis for both constant and time-varying linear equations solving is presented for comparative and illustrative purposes. Computer-simulation results substantiate the analysis.