Globally Asymptotical Stability Analysis of BAM Neural Networks with Time Delays via LMI Approach
基于LMI方法的时滞BAM神经网络的全局稳定性分析

So far many fruitful results have been obtained for stability of equilibrium points of Bidirectional Associative Memory (BAM) neural networks with axonal signal transmission delays (DBAM). A novel neural network model named as Standard Neural Network Model (SNNM) is advanced. By using state affine transformation, the DBAM neural networks are converted to SNNMs with time delays (DSNNMs). Based on some results of DSNNMs' stability, some sufficient conditions for the globally asymptotical stability of DBAM neural networks are derived, which are formulated as linear matrix inequalities (LMIs), which can be verified easily and whose conservativeness is lower. The approach proposed extends the known stability results, and can also be applied to other forms of Recurrent Neural Networks (RNNs) with (or without) time delays.