基于脉冲牵制控制的神经网络指数同步
首发时间:2018-04-10
摘要:本文研究了在随机扰动影响下部分耦合的时滞神经网络的指数同步问题,建立带有高斯白噪声的随机神经网络模型。利用外部同步,可以忽略神经网络的内部拓扑结构去解决神经网络部分耦合的情况。主要考虑脉冲牵制控制方法,利用 Lyapunov稳定性理论,推导出含时滞的部分耦合随机神经网络的指数同步充分条件,进行数值模拟,将理论结果运用于房地产投资的实际情况中,给出仿真结果,证明了以上提出的脉冲牵制控制方法以及推导出的指数同步充分条件的有效性和可行性。
关键词: 神经网络 随机扰动 指数同步 时变时滞 脉冲牵制控制
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Exponential Synchronization of Neural Networks Based on Pinning Impuliave Control
Abstract:In this paper, exponential synchronization of partially coupled delayed neural networks with stochastic disturbances is studied, stochastic neural network model with Gaussian white noise is established. Outer synchronization is used to solve the problem of partially coupled, which can ignore the internal topology of neural networks to solve the synchronization problem. Pinning impulsive control method is considered andLyapunov stability theory is used to derive the sufficient conditions of exponential synchronization for partially coupled stochastic neural networks with time delays. Numerical simulations are performed and the theoretical results are applied to the real estate investment. The simulation result shows the validity and feasibility of the derived exponential synchronization sufficient condition.
Keywords: Neural Networks Stochastic Disturbances Exponential Synchronization Time-varying Delay Pinning Impulsive Control
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