Stability analysis of discrete-time BAM neural networks based on standard neural network models

Zhang Sen-lin, Liu Mei-qin
2005 Journal of Zhejiang University: Science A  
We propose a new approach for analyzing the global asymptotic stability of the extended discrete-time bidirectional associative memory (BAM) neural networks. By using the Euler mle, we discretize the continuous-time BAM neural netwcM-ks as the extended discrete-time BAM neural networks with non-threshold activation functions. Here we present seme conditions under which the neural networks have unique equilibrium points. To judge the global asymptotic stability of the equilibrium points, we
more » ... duce a new neural network nxxlel -standard neural network model (SNNM). For the SNNMs, we derive the sufficient conditions for the global asymptotic stability of the equilibrium points, which are fonnulated as some linear matrix inequalities (LMIs). We transform the discrete-time BAM into the SNNM and apply the goieral result about the SNIVM to the determination of global asymptotic stability of the discrete-time BAM. The approach proposed extends the known stability results, has lower conservativeness, can be verified easily, and can also be applied to other forms of recurrent neural networks. Keywords: standard neural network nKxiel, bidirectional associative memory, discrete-time, linear matrix inequality, global asymptotic stability.
doi:10.1631/jzus.2005.a0689 fatcat:ah636pbnz5felimaghkhxyahyi