BACKLASH COMPENSATION IN NONLINEAR SYSTEMS USING DYNAMIC INVERSION BY NEURAL NETWORKS

A dynamic inversion compensation scheme is presented for backlash. The compensator uses the backstepping technique with neural networks (NN) for inverting the backlash nonlinearity in the feedforward path. The technique provides a general procedure for using NN to determine the dynamic preinverse of an invertible dynamical system. A tuning algorithm is presented for the NN backlash compensator which yields a stable closed‐loop system.     

采样非线性系统的动态神经网络稳定自适应控制

研究了一类采样数据非线性系统的动态神经网络稳定自适应控制方法.不同于静态神经网络自适应控制,动态神经网络自适应控制中神经网络用于逼近整个采样数据非线性系统,而不是动态系统中的非线性分量.系统的控制律由神经网络系统的动态逆、自适应补偿项和神经变结构鲁棒控制项组成.神经变结构控制用于保证系统的全局稳定性,并加速动态神经网络系统的适近速度.证明了动态神经网络自适应控制系统的稳定性,并得到了动态神经网络系统的学习算法.仿真研究表明,基于动态神经网络的非线性系统稳定自适应控制方法较基于静态神经网络的自适应方法具有更好的性能.     

非线性系统的神经网络学习控制

主要控制了一类非线性系统的神经网络学习控制问题。讨论了以迭代学习方式训练的神经网络学习控制器，在满足一定条件，可以实现一定时间内的系统输出跟踪。     

基于动态神经网络的非线性系统鲁棒观测器设计

基于动态神经对一类不确定非线性系统提出一种新的鲁棒观测器。其中无离线学习、持续激励和输出匹配条件等要求。仿真说明所提方法的有效性。     

延迟离散Hopfield型神经网络异步收敛性

离散Ｈｏｐｆｉｅｌｄ型神经网络的一个重要性质是异步运动方式下总能收敛到稳定态。同步运行方式下总能收敛到周期不超过２的极限环,它是该模型可以用于联想记忆设计,组合设计计算的理论基础,文中给出了延迟离散Ｈｏｐｆｉｅｌｄ型网络的收敛性定理,在异步运动方式下,证明了对称连接权阵的收敛性定理,推广了已有的离散Ｈｏｐｆｉｅｌｄ型网络的收敛性结果,给出了能量函数极大值点与延迟离散Ｈｏｐｆｉｅｌｄ型网络的稳定态的     

TAGUCHI'S DESIGN OF EXPERIMENT IN COMBINATION SELECTION FOR A CHAOTIC TIME SERIES FORECASTING METHOD USING ENSEMBLE ARTIFICIAL NEURAL NETWORKS

Taguchi's design of experiment, an effective approach to identify factor-level combinations, was utilized to improve the result of a proposed chaotic time series forecasting method. In the proposed method, a residual analysis using a combination of embedding theorem and ensemble neural networks was employed to forecast chaotic time series. The time series was reconstructed into proper phase space points and fed into the first neural network. The network was trained to predict the future value of phase space points and chaotic time series. The analysis of residuals of the predicted time series showed that in many events they demonstrate chaotic behaviour. The residuals were treated as a new chaotic time series and reconstructed. A new network was trained to predict the future of the residual time series. The residual analysis was repeated several times. Finally, the last network was trained using a forecast value of the original time series and residuals as input and the original time series as output. The final network was used to capture the relationship between the forecast values of the original time series and residuals and the original time series. A systematic approach is introduced using Taguchi's method to improve the combination selection of networks and their parameters. The method was applied to some real-life chaotic time series. The experimental results confirmed that the proposed method performed more effectively and accurately compared to the same method using randomized factor-level combinations and other existing forecasting methods in the literature.