针对多变量非线性离散时间系统设计多模型神经网络解耦控制器。
A multiple models neural network decoupling controller is designed to control the multivariable nonlinear discrete time system.
针对非线性离散时间系统的控制问题，提出了一种基于近似模型的多层模糊CMAC自适应控制方法。
A multi layer fuzzy CMAC adaptive control method based on an approximate model is presented in this paper for nonlinear discrete time systems.
针对一类非线性离散时间系统，根据模糊逻辑系统的逼近性质，给出了一种自适应模糊逻辑控制器的设计方法。
Based on the approximation capability of fuzzy logic systems, a design method of an adaptive fuzzy logic controller is given for a class of nonlinear discrete time systems.
对于复杂的离散时间非线性系统，提出一种基于多模型的广义预测控制方法。
A multiple model based generalized predictive control is provided for complex nonlinear discrete time system.
通过这种方法能比较容易地获得非线性系统对于实激励离散信号或模拟信号的响应时间序列。
By this method, for a real excitation discrete signal or a simulating signal, the response time series of a non-linear system can be easily obtained based on the Z-transform technique.
针对一类不确定的离散时间非线性非最小相位动态系统，提出了一种基于神经网络和多模型的直接自适应控制方法。
A direct adaptive control approach is proposed for a class of uncertain discrete time nonlinear non-minimum phase dynamical systems.
讨论隐含离散时间奇异非线性系统的精确线性化问题。
The exact linearization of a class of implicit discrete-time nonlinear singular systems is studied.
提供了离散时间广义非线性控制系统的不可测扰动的一种‘反演算法’。
The paper presents a linear algebraic solution of the dynamic disturbance decoupling problem for a generalized discrete time nonlinear system.
讨论了利用仅含一个隐层的前馈多层神经网络来辨识离散时间非线性动态系统时的模型检验问题。
This paper investigates the problem of the model validation in identifying discrete-time-nonlinear dynamic systems by using neural networks with a single hidden layer.
讨论了利用仅含一个隐层的前馈多层神经网络来辨识离散时间非线性动态系统时的模型检验问题。
This paper investigates the problem of the model validation in identifying discrete-time-nonlinear dynamic systems by using neural networks with a single hidden layer.
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