采用自适应神经网络控制方法，设计出倾转旋翼机模拟平台姿态角控制器。
An adaptive neural networks controller is designed for tilt rotor aircraft platform.
研究了一类具有未知常数控制增益的耦合大系统的直接自适应神经网络控制问题。
The problem of direct adaptive neural network control for a class of interconnected systems with unknown constant control gains is studied in this paper.
文章针对一类非线性系统，研究了一种基于回馈递推法的自适应神经网络控制方法。
An adaptive back stepping control method based on neural networks is presented for a class of nonlinear systems.
其主要特点是能够提供一个跟踪网络来辩识系统模型，进而确定控制器的网络参数，实现间接自适应神经网络控制。
Its major feature is that it can provide a tracing network to identify system model so as to determine the network parameters of the controller and realize an indirect adaptive neural network control.
结果表明，相对于常规PD控制器，该神经网络控制器具有自学习、自适应功能，位置跟踪获得了满意的控制效果。
The simulation results prove that the neural network controller has self-learning and self-adaptive ability by comparison with PD controller. The position tracking control obtains satisfactory effect.
提出利用直接自适应模糊神经网络控制一类不确定非线性混沌系统新方法。
A novel direct adaptive fuzzy neural networks (FNNs) controller for a class of uncertain nonlinear chaotic system is presented.
本文根据变量泵的具体情况，为其设计了自适应的神经网络模糊控制器，实现了变量泵的智能控制。
This paper describes that the combination of neural networks and fuzzy controller make the designing an intelligent controller for variable piston pumps become feasible and realistic.
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.
本文提出了一种基于改进的神经网络(MNN)的自适应闭环功率控制算法，该方法平滑了移动信道衰落的影响，使基站接收到的小区中所有用户的信号功率相等。
In this paper, a Modified Neural Network (MNN) based power controller is proposed to smoothen out the fast fading and keep the received signal power from each user constant at the base station.
基于模糊神经网络算法研究了非线性系统的噪声消除问题，设计了一类非线性自适应逆噪声消除控制器。
Based on Fuzzy Neural Network, the noise canceling problem of the nonlinear system was studied. A type of nonlinear adaptive noise controller was proposed.
基于数据融合的思想，提出一种非线性系统的自适应神经网络模糊控制器的设计方法。
Based on data fusion method, an adaptive neuro-fuzzy controller of nonlinear systems is presented.
鉴于模糊神经网络具有良好的非线性特性、学习能力、自适应能力和抗干扰能力，本文将模糊神经网络技术引入到高速公路入口匝道控制中。
Due to the traits of nonlinear, capacity of study, adaptivity and anti-interference, neural-fuzzy network is suitable for the control of ramp metering.
第二种方法则是基于误差通道在线辨识的神经网络自适应控制方法。
The second method is the adaptive control by using neural networks based on online secondary path modeling.
本文针对船舶操纵这种非线性、时变参数控制对象，提出了一种采用神经网络自适应PID控制方案。
In this paper, according to nonlinear and time-varying parameter of ship maneuvering, the scheme of neural network adaptive PID control is proposed.
提出了一种自适应r BF神经网络功率控制方案。
An adaptive radial basis function neural network (ARBFNN) power control scheme is proposed.
仿真结果表明，神经网络、自适应逆控制方法可以成功地解决装置中现有的控制问题，并取得良好的效果。
The results of simulation show that the existing control problem of the plant can be successfully solved by neural network, adaptive inverse control and the result is good.
针对包装印刷传动位置伺服系统，介绍一种基于共轭梯度学习算法的神经网络自适应PID控制方法。
The paper proposes an adaptive neural network PID controller based on weighlearning algorithm using the gradient descent method for the AC position servosystem of binding and printing.
研究了非线性的神经网络模型参考自适应控制器设计问题。
The design of adaptive controller for the chaotic neural network model reference is studied.
本文研究一类非线性神经网络自适应控制系统，提出一种基于双误差——辨识误差和跟踪误差的新控制方案。
A class of nonlinear neural network adaptive control systems is studied and a new design concept based on double errors was proposed in this paper.
本文提出了用神经网络模型参考自适应控制器对加工过程进行控制的方法。
This paper proposes a method of using neural network model reference adaptive controller to control machining process.
提出一种在用户-网络接口处利用对角递归神经网络(DRNN)作为自适应预测器，实现AT M网络自适应拥塞控制的模型。
This paper presents an adaptive congestion control model in ATM networks at the user to network interface by using a diagonal recurrent neural network (DRNN) as an predictor.
针对传统的PID控制算法很难获得比较理想的控制效果的问题，提出一种基于BP神经网络的自适应pid控制算法。
Aiming at the problem of traditional PID control algorithm is difficult to get ideal control effect, an adaptive PID control algorithm based on BP neural network is proposed.
各控制子系统的设计采用自适应控制和神经网络相结合的方法，所提出的参数和权重的自适应调整律保证系统的稳定性。
Each subsystem is designed using adaptive control with neural network compensation. The stability of the system is guaranteed by the proposed parameter and weight turning law.
利用对角递归神经网络在线自适应调整PID控制器的参数，从而使系统的静态和动态性能指标较为理想。
DRNN is used to adjust the parameters of PID control on-line, accordingly it can make static and dynamic performance index comparatively ideal.
接着，结合其存在的问题，对动态递归神经网络、R BF神经网络和自适应逆控制进行了算法研究。
Then, aiming at the existing problem, the algorithm of dynamic recurrent neural network, RBF neural network and adaptive inverse control is studied in the paper.
本文首先引入一个能方便进行在线自适应的扩展控制对象自适应神经网络模型，在此基础上提出一种噪声有源控制的自适应神经网络方法。
This paper firstly introduces an extended plant adaptive neural network model which can be on-line adapted conveniently, then presents a method of active noise control using adaptive neural networks.
首先根据模型参考自适应控制理论，将模型逆与在线神经网络结合，设计了神经网络自适应姿态控制系统。
The neural network adaptive attitude control system is designed based on model reference adaptive control theory and combination of online neural network with augmented model inversion.
提出一种基于动态递归神经网络的自适应pid控制方案，该控制系统由神经网络辨识器和神经网络控制器组成。
This paper presents an adaptive PID control scheme based on dynamic recurrent neural network. The control system is consisted of the neural network identifier and the neural network controller.
提出一种基于动态递归神经网络的自适应pid控制方案，该控制系统由神经网络辨识器和神经网络控制器组成。
This paper presents an adaptive PID control scheme based on dynamic recurrent neural network. The control system is consisted of the neural network identifier and the neural network controller.
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