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.
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。
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.
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。
In this paper, according to nonlinear and time-varying parameter of ship maneuvering, the scheme of neural network adaptive PID control is proposed.
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。
Based on the study of BP neural network and PID controller, a single neuron adaptive PSD algorithm is presented.
在对BP神经网络PID控制器系统研究的基础上，提出了单神经元的自适应PSD算法。
A neural network PID control algorithm is presented to improve the PID adaptive control performance.
为提高PID控制的自适应性能，提出了一种神经网络PID自适应控制算法。
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。
The computer simulation results show that the neural network supervisory control has more adaptive ability and robust than that of a PID controller, and the control precision is increased.
对压水堆功率控制过程进行了计算机仿真，仿真结果表明，与传统的PID控制相比，神经网络监督控制具有较强的自适应能力和鲁棒性，有效地提高了控制系统的精度。
We study expert PID control, fuzzy adaptive PID control, RBF neural network PID control, internal control based on RBF neural networks.
研究了专家PID控制、模糊自适应PID控制、基于RBF神经网络整定的PID控制、基于RBF神经网络的内模控制。
Simulation res ults prove that this new multi-step prediction based on PID-like neural network control system can effectively attenuate random noise interference and is more robust and adaptive.
仿真实验表明，基于多步预测的PID型神经网络控制系统能有效抑制随机干扰，具有较强的适应性和鲁棒性。
An adaptive PID control scheme based on dynamic recurrent neural network is presented. 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。
A new type of adaptive PID controller using diagonal recurrent neural network (DRNN) is presented. An on-line learning algorithm based on PID parameter self-tuning method is given.
提出了一种基于对角回归神经网络的PID控制器结构，给出了PID参数在线自整定的学习控制算法。
Neural network PID adaptive control scheme was used to enhance the performance of piezoelectric phase shifter by improving the nonlinear performance and depressing the interference of environment.
采用了神经网络PID自适应控制方法，改善了非线性，降低了环境干扰的影响，提高了压电式 移相器的性能。
The emulational results show that the improved BP neural network PID enables the convergence to be faster and the system has strong robustness and self-adaptive.
仿真结果表明，改进BP神经网络PID使收敛变得更快，而且系统具有较强的鲁棒性和自适应能力。
The paper presents a new adaptive control system based on PID-like neural network, gives the learning algorithm of this new controller and analyses stability of the control system.
本文提出了一种新的基于PID型神经网络的自适应控制系统，给出了神经网络控制器的学习算法和控制系统的稳定性分析。
To improve robustness of control system, the paper analyses the influence of nonlinear factors and introduces the neural network PID auto adaptive control to reduce the influence.
为了增强系统的鲁棒性，论文在分析非线性因素对系统的影响后，利用神经pid自适应控制实现了对这些因素的有效抑制。
The method of adaptive neural-PID control that combines neural network and PID control is put forward in allusion to shortcoming of conventional PID control for nuclear steam generator water level.
本文针对传统的核动力蒸汽发生器水位PID控制方法存在的缺点，将神经网络方法与PID控制的结构结合起来，提出了核动力蒸汽发生器水位神经自适应PID控制方法。
Simulation experiment compared with the double-loop motor subject to adaptive PID control, fuzzy control, neural-network control and conventional PID control is presented.
并与国内外研究较多的无刷直流电机的基于自适应PID、模糊控制、神经网络控制、PID控制的双闭环控制系统进行仿真对比实验。
Simulation experiment compared with the double-loop motor subject to adaptive PID control, fuzzy control, neural-network control and conventional PID control is presented.
并与国内外研究较多的无刷直流电机的基于自适应PID、模糊控制、神经网络控制、PID控制的双闭环控制系统进行仿真对比实验。
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