神经网络为未知非线性动态系统的建模提供了一条新途径。
Multilayered neural network offers a new exciting alternative for modelling unknown nonlinear dynamical system.
本文探讨了只用单个隐含层的前向神经网络对未知非线性动态系统的识别。
This paper investigates the identification of unknown nonlinear dynamical system using multilayered feedforward neural network with a single hidden layer.
仿真实例进一步表明,采用神经网络建立未知非线性动态系统的在线模型具有可行性。
The simulation results are presented to demonstrate that the model of an unknown nonlinear dynamical system is built with the multilayered feedforward neural network model.
对一类非线性多变量未知动态系统,提出了一种模糊自适应控制策略。
A fuzzy adaptive tracking control scheme for a class of unknown multivariable nonlinear systems is presented.
本文针对具有未知参数不确定和干扰的严格反馈型的非线性系统鲁棒自适应控制提出了一种新的设计方法,即动态面控制。
We propose a new method for robust adaptive back stepping control of nonlinear systems with parametric uncertainties and disturbances in the strict feedback form.
针对带有未知有界噪声的非线性动态系统的鲁棒辨识问题,提出了一种新的非线性动态系统的集员辨识算法。
A new set membership identification algorithm was proposed for the robust identification problem of nonlinear dynamic systems with unknown but bounded noises.
采用RBF神经网络逼近系统未知的非线性函数,引入滑模误差对其权值进行在线自适应调整,改善动态性能。
RBF neural network is proposed to approximate unknown nonlinear function. Sliding mode error is used to adaptively tune its weights online. Dynamics performance is improved.
对于模型未知的复杂非线性系统或是动态特性常变的控制对象,两者的不依赖精确数学模型的控制特性具有无可比拟的优势。
They have superiority in the uncertain model and non-linear system and time variant system control, due mostly to their being independent of precise mathematical model.
对于模型未知的复杂非线性系统或是动态特性常变的控制对象,两者的不依赖精确数学模型的控制特性具有无可比拟的优势。
They have superiority in the uncertain model and non-linear system and time variant system control, due mostly to their being independent of precise mathematical model.
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