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Adaptive Synchronization for Unknown Chaotic Systems with Fuzzy-Neural Network Observer

机译:具有模糊神经网络观测器的未知混沌系统的自适应同步

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This investigation applies the adaptive fuzzy-neural observer (AFNO) to synchronize a class of unknown chaotic systems via scalar transmitting signal only. The proposed method can be used in synchronization if nonlinear chaotic systems can be transformed into the canonical form of Lur'e system type by the differential geometric method. In proposed approach, the receiver states can be reconstructed from one transmitted state using AFNO design. The adaptive fuzzy-neural network (FNN) in AFNO is adopted to model the nonlinear term in the transmitter. Additionally, an observer is designed to estimate the other states of the master. Synchronization is achieved when all states are observed. The proposed scheme can adaptively estimated the transmitter states using AFNO, even if the transmitter changes into another chaotic system. Simulation results confirm that the proposed AFNO design is valid.
机译:这项研究应用自适应模糊神经观测器(AFNO)仅通过标量传输信号来同步一类未知混沌系统。如果非线性混沌系统可以通过微分几何方法转换为Lur'e系统类型的规范形式,则该方法可用于同步。在提出的方法中,可以使用AFNO设计从一种传输状态重构接收器状态。 AFNO中的自适应模糊神经网络(FNN)用于对变送器中的非线性项进行建模。此外,还设计了一个观察器来估计主机的其他状态。当观察到所有状态时,即可实现同步。所提出的方案可以使用AFNO自适应地估计发射机状态,即使发射机变为另一个混沌系统也是如此。仿真结果证实了所提出的AFNO设计是有效的。

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