Signum-function array activated ZNN with easier circuit implementation and finite-time convergence for linear systems solving

A new type of Zhang neural network (ZNN), which is activated by the signum-function array, is proposed for linear systems solving. Such a signum-function array activated ZNN is developed on the basis of a vector-valued error function instead of a scalar-valued norm based energy function. Besides, a theorem is provided to illustrate the excellent finite time convergence property of the new-type ZNN. In addition, the corresponding circuit schematic of the signum-function array activated ZNN is given. For better illustration, a representative simulative example is presented and the corresponding simulation result is shown to substantiate the efficacy of the proposed new-type ZNN for linear systems solving. Besides, the comparative simulation result further shows the desired finite-time convergent performance. (C) 2017 Elsevier B.V. All rights reserved.