基于轨迹线性化方法(TLC)及神经网络技术研究了一种新的直接自适应TLC控制方案。
This paper presents a novel nonlinear adaptive control method based on trajectory linearization control method (TLC) and neural networks.
在全状态反馈的前提下，设计了一种基于在线神经网络和反馈线性化的非线性直接自适应控制器。
A nonlinear direct adaptive controller based on online neural network and feedback linearization is designed by precondition of all state feedback.
与传统线性化的迭代反演比较，神经网络反演能够克服传统方法的不足、获得更好的反演结果。
Compared to the traditional iterative inversion method through linearization, the neural network inversion is able to overcome disadvantages of the traditional inversion and obtain better results.
根据反馈线性化理论，讨论了神经网络自适应非线性动态逆控制设计。
A discussion is devoted to the design of a self adaptive and nonlinear dynamic neural network inversion controller according to the feedback linearization theory.
并简单地给出相应神经网络模型线性化后得到的特征方程。
And we also give the corresponding characteristic equation of linearization discrete neural network model.
文中讨论了使V CO线性化的电抗补偿原理并给出了用多节电抗网络补偿的VCO设计实例。
The principle of linearizing VCO by reactance compensation is discussed. The design of the practical VCO is given.
然后，针对输入—输出反馈线性化得到的数学模型中的非线性项，本文利用神经网络来对该部分进行逼近。
Then a neural network is employed to approximate the non-linear component of the input-output linearized system.
详细分析了固定拓扑法、线性化友网络法等涉及到的关键技术。
Critical technologies related to fix topology method, linear friend-network method and so on were analyzed in detail.
详细分析了固定拓扑法、线性化友网络法等涉及到的关键技术。
Critical technologies related to fix topology method, linear friend-network method and so on were analyzed in detail.
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