The results showed that control performance of Double-Weighted neural network controller is well than BP neural network controller.
结果表明，与BP网络相比，双权值神经网络能对系统进行更好的控制。
The simulation model and result are given under the environment of MATLAB. The fluctuation of wind speed can be controlled and the disturbance can be cancelled by BP neural network controller.
在MATLAB环境下给出了用BP网对变速风力发电机控制的仿真模型和仿真结果，显示采用神经网控制器控制有很好的抗风速突然波动的作用，能有效地抑制扰动。
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。
The emphasis is placed on the design of the fuzzy controller, the method of determining system′s gain parameter and the method of realizing BP neural network.
重点研究了模糊控制器的设计、系统增益参数的确定方法和BP神经网络的实现方法。
The part of neural network of the intelligent self-tuning PID controller based on BP network learns the learning sample.
由设计出的基于BP神经网络的智能自整定PID控制器的神经网络部分对学习样本进行学习。
This paper presents a neural network control system based on CMAC, which consists of a CMAC neural network controller and a BP network model identifier.
提出了一种基于CMAC神经网络控制系统，该系统由CMAC神经网络控制器和BP模型辨识网络组成。
Analyzing the integral splitting PID algorithm, and melting the wide-used PID controller and the automatic learning neural network, got a PID control algorithm based on the BP network.
分析了积分分离pid控制算法，在此基础上，将应用最广泛的PID控制器与具有自学习功能的神经网络相结合，得到了基于BP神经网络的PID控制算法。
It is proposed a magnetic bearing BP neural network PID controller design.
提出磁轴承bp神经网络PID控制器设计方案。
The parameters of the fuzzy neural network controller are optimized by the mixed learning methods with BP algorithm and Simulated Annealing algorithm which improves BP algorithm.
该系统的控制器采用模糊神经网络控制器，它的控制器参数采用模拟退火算法全局优化来对BP算法进行改进的混合方法。
A collateral controller based on BP neural network (NN) and PID was designed for fatigue tester system.
针对疲劳试验机控制系统，设计了基于BP神经网络和PID的并行控制器。
The result of adaptability experiment for valving controller based on artificial neural network is presented in this paper. The controller is trained by off-line BP network.
本文给出了用人工神经网络硬件构成的汽门控制器的适应性实验结果，控制器采用BP网络离线训练。
Three-layer BP neural network models are used for the internal model and the reverse model of the controlled system in the controller.
该控制器的正模型和逆模型都以三层BP神经网络为主体，实现对SVC及电网的动态描述和对SVC的控制。
Based on the mathematic model of PMSM, a combination of an improved BP neural network and a general PID controller is used in its speed control system.
在分析永磁同步电机数学模型的基础上，采用改进型BP神经网络与传统PID控制相结合作为速度控制器，应用于永磁同步电机调速系统中。
In this paper a new arm-driven inverted pendulum is presented, and a 4-input 1-output 3-layer BP neural network is designed to approach the first-level arm-driven inverted pendulum controller.
本文针对一种新型的倒立摆系统，设计了一个四输入单输出三层BP网络，并对一级旋转倒立摆控制器进行逼近。
In this paper a new arm-driven inverted pendulum is presented, and a 4-input 1-output 3-layer BP neural network is designed to approach the first-level arm-driven inverted pendulum controller.
本文针对一种新型的倒立摆系统，设计了一个四输入单输出三层BP网络，并对一级旋转倒立摆控制器进行逼近。
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