A New RNN Model With a Modified Nonlinear Activation Function Applied to Complex-Valued Linear Equations

Abstract

In this paper, an improved Zhang neural network (IZNN) is proposed by using a kind of novel nonlinear activation function to solve the complex-valued systems of linear equation. Compared with the previous ZNN models, the convergence rate of the IZNN model has been accelerated. To do so, a kind of novel nonlinear activation function is first proposed to establish the novel recurrent neural network. Then, the corresponding maximum convergent time is given according to the randomly generated initial error vector, and the theoretical proof is described in detail in this paper. Finally, the experiment results illustrate that the new recurrent neural network using the proposed activation function has higher convergence rate than the previous neural networks using the linear activation function or the tunable activation function.

Keywords

• Recurrent neural network
• convergence rate
• finite time
• complex-valued systems of linear equation
• novel nonlinear activation function