Investigation stability of a delayed Bidirectional Associative Memory (BAM) neural network

Document Type : Original Paper

Authors

1 PhD student in Mathematics, Department of Mathematical Sciences and Statistics, Birjand University, Iran.

2 Assistant Professor, Department of Mathematical Sciences and Statistics, Birjand University, Iran

3 Professor, Department of Electrical and Computer Engineering, Wisconsin-Platteville University, USA

4 Associate Professor, Department of Mathematical Sciences and Statistics, Birjand University, Iran.

Abstract

In this paper, the stability of a delayed Bidirectional Associative Memory (BAM) neural network consisting of two layers has been investigated. The approach includes linearization of the BAM neural network, obtaining the characteristic equation, analyzing the nature of its roots, and obtaining the condition for the systems' stability. The results show that the neural network is asymptotically stable when the eigenvalues have a negative real part. Next, the effect of delay in creating oscillation in the system was investigated, and the relevant parameter was obtained. Compared to the other published work in this area, an advantage of the proposed approach is its ability to identify the system's stability in a much more straightforward and less complicated method. Finally, a 2-layer neuron network simulation, with three neurons in each layer, using Simulink software (affiliated with MATLAB), is presented. The simulation results confirm the efficiency of the proposed method.

Keywords

Main Subjects

References

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Journal of Advanced Mathematical Modeling
Volume 11, Issue 2
June 2021
Pages 302-316

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History

  • Receive Date: 07 September 2020
  • Revise Date: 31 March 2021
  • Accept Date: 06 May 2021

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