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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >New Results on Stability Analysis for Delayed Markovian Generalized Neural Networks With Partly Unknown Transition Rates
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New Results on Stability Analysis for Delayed Markovian Generalized Neural Networks With Partly Unknown Transition Rates

机译：具有未知转移率的延迟马尔可夫广义神经网络稳定性分析的新结果

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摘要

The stability of delayed Markovian generalized neural networks is studied where the transition rates of the modes are partly unknown. The partly unknown transition rates generalize the traditional works that are with all known transition rates. Then, a Lyapunov-Krasovskii functional (LKF) with a delay-product-type (DPT) term is constructed. The DPT term is not only simple but also fully utilizes the information of time delay. Based on the new DPT LKF, stability criteria are presented, which are with lower computational complexity and less conservative. In the end, the validity and superiorities of the analytical results are verified by several examples.
机译：研究了模态跃迁速率部分未知的时滞马尔可夫广义神经网络的稳定性。部分未知的转换率概括了所有已知转换率的传统作品。然后，构造了具有延迟积类型（DPT）项的Lyapunov-Krasovskii泛函（LKF）。 DPT术语不仅简单，而且充分利用了时延信息。基于新的DPT LKF，提出了稳定性标准，该标准具有较低的计算复杂度和较低的保守性。最后，通过几个实例验证了分析结果的有效性和优越性。

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