Improved Zhang neural network with finite-time convergence for time-varying linear system of equations solving

This paper proposes an improved Zhang neural network (IZNN) for time-varying linear system of equations solving. Such a neural network is activated by an array of continuous sign-bi-power function. Theoretical analysis is provided to show the desired finite-time convergence property of the proposed IZNN. As compared to Zhang neural network activated by an array of discontinuous signum-function, the solution synthesized by the proposed neural network can converge to theoretical solution, while the solution synthesized by the latter oscillates to some extent around the equilibrium point Moreover, the remarkable finite-time convergence of the proposed IZNN model is corroborated by a simulative example. Simulation results also demonstrate that the proposed neural network is more suitable in engineering applications than Zhang neural network activated by the array of discontinuous signum-function. (C) 2019 Elsevier B.V. All rights reserved.