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Extended Woo Synchronization Control for Switched Neural Networks with Multi Quantization Densities Based on a Persistent Dwell-Time Approach

机译:基于持久停留时间方法的多量化密度的交换神经网络扩展了Woo同步控制

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This paper thoroughly investigates the synchronization control issue for the switched neural networks. The more comprehensive comparatively switching rule, persistent dwell-time, is applied to actuate the aforementioned neural networks. For tackling the problem caused by the transmission of tremendous data, the quantizer is utilized. The objective is to establish the mixed controller with multi quantization densities for the synchronization error neural networks to meet the various accuracy requirements of the transmitted data. Whereafter, the sufficient conditions of the extended H-infinity performance and global uniform exponential stability for the synchronization error neural networks are constructed. Conclusively, the capability of the proposed mixed controller is elucidated through a numerical example.
机译:本文彻底调查了交换神经网络的同步控制问题。相对开关规则持久的停留时间更加全面,应用于致动上述神经网络。为了解决巨大数据传输引起的问题,使用量化器。目的是建立具有多量化密度的混合控制器,用于同步误差神经网络以满足所传输数据的各种精度要求。然后,构造了对同步误差神经网络的扩展H-Infinity性能和全局统一指数稳定性的充分条件。结论,通过数值示例阐明了所提出的混合控制器的能力。

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