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To facilitate stability analysis of discrete-time bidirectional associative memory (BAM) neural networks, they were converted into novel neural network models, termed standard neural network models (SNNMs), which interconnect linear dynamic systems and bounded static nonlinear operators. By combining a number of different Lyapunov functionals with S-procedure, some useful criteria of global asymptotic stability and global exponential stability of the equilibrium points of SNNMs were derived. These stability conditions were formulated as linear matrix inequalities (LMIs). So global stability of the discrete-time BAM neural networks could be analyzed by using the stability results of the SNNMs. Compared to the existing stability analysis methods, the proposed approach is easy to implement, less conservative, and is applicable to other recurrent neural networks. 相似文献
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在对颗粒浆无菌自动灌装系统初始设计方案的基础上,对方案进行了改进和优化设计,有效解决了灌装过程中颗粒浆给料不稳定及灌装精度不够高等问题。此方案在实际工业生产中得到了运用,不仅实现了颗粒浆高效自动无菌灌装,解决了食品灌装过程中的二次污染问题,也为企业带来了较大的经济效益和社会效益,实践证明了该方案的正确性和可行性。 相似文献
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Studies on the stability of the equilibrium points of continuous bidirectional associative memory (BAM) neural network have yielded many useful results. A novel neural network model called standard neural network model (SNNM) is ad- vanced. By using state affine transformation, the BAM neural networks were converted to SNNMs. Some sufficient conditions for the global asymptotic stability of continuous BAM neural networks were derived from studies on the SNNMs’ stability. These co… 相似文献
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