基于Chebyshev正交函数神经网络的混沌系统鲁棒自适应同步

提出了基于Chebyshev正交函数神经网络的不确定性混沌系统的鲁棒自适应同步方法.首先,本文提出了正交函数神经网络的网络结构,分析了利用Chebyshev正交多项式形成神经网络的机理.利用Lyapunov稳定性定理确定正交函数神经网络控制器的权值更新规则,并保证权值误差和跟踪误差的有界性.该方法能克服不确定性对混沌系统同步的破坏,实现了良好的同步效果.在本文最后,针对Lorenz系统进行了数值计算,数值计算结果表明了所给方法的有效性.

Robust adaptive synchronization of chaotic systems based on Chebyshev orthogonal function neural network

By using Chebyshev orthogonal neural network, we propose a robust adaptive synchronization method for a class of uncertain chaotic system. The structure of the orthogonal function neural network is first introduced, and then, the principle of orthogonal neural network is analyzed by using Chebyshev orthogonal polynomials. We derive the adaptive rule for the weights of neural network by using Lyapunov stability theorem, and ensure that both adapted weight errors and the tracking error are bounded. The simulation results show that the proposed approach can effectively eliminate the disruption of perturbation. Finally, a Lorenz system is employed to verify the effectiveness of the proposed method, and the simulation results are shown in the end of this paper.