A New RNN Model With a Modified Nonlinear Activation Function Applied to Complex-Valued Linear Equations | IEEE Journals & Magazine | IEEE Xplore

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


The evolution process of the residual errors for different ZNN models.

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 eq...Show More

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.
The evolution process of the residual errors for different ZNN models.
Published in: IEEE Access ( Volume: 6)
Page(s): 62954 - 62962
Date of Publication: 18 October 2018
Electronic ISSN: 2169-3536

Funding Agency:


References

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