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- W2384169175 abstract "So far many fruitful results have been obtained for stability of equilibrium points of continuous time bidirectional associative memory (BAM) neural network. A novel neural network model termed standard neural network model (SNNM) was advanced. By combining a number of different Lyapunov functional with S-procedure, some useful linear/bilinear matrix inequality (LMI/BMI)-based criteria for the global asymptotic stability and global exponential stability of the SNNM were derived. By using state affine transformation, the BAM neural networks were converted to the SNNMs. Based on some results of the SNNMs’ stability, some sufficient conditions for the global asymptotic stability and global exponential stability of the continuous time BAM neural networks were obtained, which were formulated as easily verifiable LMIs or BMIs, whose conservativeness is relatively low. The proposed approach improves the known stability results, and can also be applied to other forms of recurrent neural networks." @default.
- W2384169175 created "2016-06-24" @default.
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- W2384169175 date "2005-01-01" @default.
- W2384169175 modified "2023-09-26" @default.
- W2384169175 title "Stability analysis of continuous time BAM neural networks: an LMI/BMI approach" @default.
- W2384169175 hasPublicationYear "2005" @default.
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