New DTZNN model for future minimization with cube steady-state error pattern using Taylor finite-difference formula

Abstract

In this paper, a discrete-time Zhang neural network (DTZNN) model, discretized from continuous-time Zhang neural network, is proposed and investigated for performing the online future minimization (OFM). In order to approximate more accurately the 1st-order derivative in computation and discretize more effectively the continuous-time Zhang neural network, a… (More)

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