Description: 该程序是基于BP网络的PID控制系统。系统由两部分构成：经典的PID控制器和神经网络。-that the procedure was based on BP network PID control system. System consists of two parts : the classic PID controller and neural networks. Platform: |
Size: 1024 |
Author:周龙 |
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Description: 基于BP神经网络整定的PlD控制，神经网络，根据系统的运行状态，调节PID控制器的参数，以期达到某种性能指标
的最优化，使输出层神经元的输出状态对于控制器的三个可调参数-BP neural network based on the PLD-tuning control, neural network, in accordance with the system running, adjusting the parameters of PID controller with a view to attaining certain performance metrics are optimized so that the output layer neuron output state for the three controllers can be tune parameters Platform: |
Size: 1024 |
Author:feiyang |
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Description: bp神经网络用于PID控制器的参数优化,程序可以直接运行,具有很好的优化效果!-bp neural network for parameter optimization of PID controller, the program can be directly run, with good effect to optimize! Platform: |
Size: 1024 |
Author:liyan |
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Description: BP神经网络结构PID控制器设计，严格按照增加动量项写的MATLAB程序。-Structure of BP neural network PID controller design, in strict accordance with the increasing momentum of procedures written in MATLAB. Platform: |
Size: 1024 |
Author:曹顺 |
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Description: 采用神经网络控制的方法。利用人工神经网络的自学习这一特性，并结合传统的PID控制理论，构造神经网络PID控制器，实现控制器参数的自动调整-Control method using neural network. Artificial neural network to learn the characteristics of the self, combined with the traditional PID control theory, structural neural network PID controller, automatically adjust the controller parameters
Platform: |
Size: 1024 |
Author:孙丽媛 |
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Description: 本文把神经网络技术应用在PID控制中，充分利用神经网络具有非线性函数逼近能力构造神经网络PID自整定控制器。-This paper, the neural network technology used in PID control, the full use of neural network structure with nonlinear function approximation capability PID self-tuning neural network controller. Platform: |
Size: 97280 |
Author:zhuzhu |
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Description: 人工神经网络(Artificial Neural Network)是从生理角度对智能的模拟，具有极
高的学习能力和自适应能力，能够以任意精度逼近任意函数，完成对系统的仿真；
而遗传算法是对自然界生物进化过程的模拟，具有极强的全局寻优能力，这两种
算法都是当下研究较多的智能方法。将这两种方法与常规的 PID 控制相结合，
构成智能 PID 控制器，使其具有参数自整定、自适应的能力，以适应复杂环境
下的控制要求，这一思路对提高控制效果具有很好的现实意义。
-Artificial Neural Network (ANN) is an imitation of the intelligence by the point of physiological. It has a high capacity of learning and adaptive, can approximate any function to arbitrary accuracy, and complete the simulation of the system. The Genetic
algorithm is a simulation of natural biological evolution, which has a strong ability of
global optimization. These two algorithms are more intelligent method of current
research. The idea of combining these two methods with the conventional PID
controller to be a intelligent controller with the abilities of parameter auto-tuning and
adaptive for the requirements of the complex environment, has a high practical
significance of improving the control effect.
Platform: |
Size: 661504 |
Author:baijiaxuan |
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Description: 运用常规的PID控制算法很难达到人们所要求的控制效果。采用改进的BP神经网络算法进行改进具有以任意精度逼近非线性函数的能力，而且通过它的自身的学习，可以找到某一最优控制率下的PID控制器参数，使其具有更好的鲁棒性和自适应的能力。-Using conventional PID control algorithm is difficult to live up to the required control effect. The improved BP neural network algorithm to improve accuracy with an arbitrary nonlinear function approximation ability, but also through its own learning, you can find an optimal control rate of PID controller parameters, to make it better robustness and adaptive capacity. Platform: |
Size: 5120 |
Author:小静 |
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Description: 针对传统的PID 控制器参数固定而导致在控制中效果差的问题，提出一种基于模糊RBF 神经网络智能PID 控制器的设计方法。
该方法结合了模糊控制的推理能力强与神经网络学习能力强的特点，将模糊控制与RBF 神经网络相结合以在线调整PID 控制器参数，整
定出一组适合于控制对象的kp. ki. kd 参数。将算法运用到电机控制系统的PID 参数寻优中，仿真结果表明基于此算法设计的PID 控制器改善了电机控制系统的动态性能和稳定性。-Fixed for the traditional PID controller parameters lead to poor results in the control issue, based on fuzzy RBF neural network intelligent PID controller design method.
The method combines fuzzy control powers of reasoning and neural network learning ability and fuzzy control with RBF neural network combined line tuning of PID controller parameters, the entire
Identified a group suitable for the control object kp ki kd parameters. PID parameter optimization algorithm is applied to the motor control system, the simulation results show that the PID controller design based on this algorithm improves the dynamic performance and stability of the motor control system. Platform: |
Size: 418816 |
Author:张文 |
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Description: 一种新的PID型模糊神经网络控制器的研究,
带Smith预估器的改进遗传算法
-A New PID-type Fuzzy Neural Network Controller based on Genetic Algorithm with improved Smith Predictor Platform: |
Size: 621568 |
Author:徐文 |
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Description: The paper deals with a controller design for the nonlinear processes using genetic
algorithm and neural model. The aim was to improve the control performance using
genetic algorithm for optimal PID controller tuning. The plant model has been
identified via an artificial neural network from measured data. The genetic algorithm
represents an optimisation procedure, where the cost function to be minimized
comprises the closed-loop simulation of the control process and a selected
performance index evaluation. Using this approach the parameters of the PID
controller were optimised in order to become the required behaviour of the control
process. Testing of quality control process was realized in simulation environment of
Matlab Simulink on selected types of nonlinear dynamic processes.-The paper deals with a controller design for the nonlinear processes using genetic
algorithm and neural model. The aim was to improve the control performance using
genetic algorithm for optimal PID controller tuning. The plant model has been
identified via an artificial neural network from measured data. The genetic algorithm
represents an optimisation procedure, where the cost function to be minimized
comprises the closed-loop simulation of the control process and a selected
performance index evaluation. Using this approach the parameters of the PID
controller were optimised in order to become the required behaviour of the control
process. Testing of quality control process was realized in simulation environment of
Matlab Simulink on selected types of nonlinear dynamic processes.
Platform: |
Size: 135168 |
Author:samir |
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Description: This file is about A neural network approach in order to perform an intelligent and adaptive controller with PID structure.
-This file is about A neural network approach in order to perform an intelligent and adaptive controller with PID structure.
Platform: |
Size: 1024 |
Author:rouhollah |
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Description: 基于BP神经网络的PID控制方法设计控制器，通过BP神经网络与PID的控制相结合的神经网络控制基本原理和设计来自适应的功能调节PID的的三个参数，并根据被控对象的近似数学模型来输出输入与输出并分析BP神经网络学习速率η，隐层节点数的选择原则及PID参数对控制效果的影响。-based on BP neural network PID control method designed controller, through the BP neural network PID control with a combination of neural network control basic principles and design features adaptively adjusting the PID of the three parameters, and based on the controlled object approximate mathematical model to analyze the output and the input and output BP neural network learning rate η, hidden layer nodes and PID parameter selection principle effect of the control . Platform: |
Size: 1024 |
Author:熊智 |
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Description: 多变量输入、输出、多干扰、非线性和强耦合的复杂系统控制是一个比较困难的问题，常用的控制器可能因为多变量耦合问题难以控制系统。PID神经元网络是一种多层前向神经元网络，它的各层神经元个数、连接方式、连接权值是按照PID控制规律的已有原则和经验确定的，是一种动态的符合控制系统的前向网络。但是由于PID网络初始权值随机取值的原因，每次控制的效果都有所差别，个别情况下控制效果还比较差。本案例研究了基于PID神经元的多变量耦合系统控制，并用PSO算法来优化控制器以取得更好的控制效果。-Multivariable input, output, and more interference, complex nonlinear and strong coupling system control is a more difficult problem, commonly used multivariable controller may be because the problem is difficult to control the coupling system. PID neural network is a multi-front to neural networks, its number of neurons in each layer, connection, connection weights are in accordance with the existing principles and experience to determine the PID control law is a dynamic compliance before the control system to the network. However, due to the random initial weights of the network PID values of reason, every time there are differences in the effect of control and in some cases the control effect is still relatively poor. This case study of multivariable coupling system based on PID control neurons, and with PSO algorithm to optimize the controller to achieve better control effect. Platform: |
Size: 14336 |
Author:wujiewen |
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Description: 基于BP神经网络的PID控制器结构,控制器由两部分组成：一是常规PID控制器，用以直接对对象进行闭环控制，且3个参数在线整定；二是神经网络NN，根据系统的运行状态，学习调整权系数，从而调整PID参数，达到某种性能指标的最优化。-基于BP神经网络的PID控制器结构,控制器由两部分组成：一是常规PID控制器，用以直接对PID controller structure BP neural network, the controller consists of two parts: First, a conventional PID controller for closed loop control of the direct object, and three-line parameter tuning the second is the neural network NN, operate in accordance with the system state, learning to adjust the weights to adjust the PID parameters to achieve certain performance optimization. Platform: |
Size: 2048 |
Author:马腾 |
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Description: The controller is built by MATLAB/Simulink. PID人工神经网络控制算法(Neural Network with PID controller is built by MATLAB/Simulink. This controller can be used in motor drive and other actuators in industry.) Platform: |
Size: 9216 |
Author:yuzou |
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