Description: 基于神经网络的PID控制不是用神经网络来整定PID的参数，而是用神经网络直接作为控制器，通过训练神经网络的权系数间接地调整PID参数。-based on neural network PID control is not using neural networks to PID parameters, Rather, as a neural network controller directly, through the training of the neural network weights indirectly PID parameters to adjust. Platform: |
Size: 69632 |
Author:戚伟 |
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Description: 有脉冲干扰 下的柔性机械臂的模糊控制方法。采用神经网络训练出来的T—S模糊控制器，控制效果不错。只有仿真模块，没有文字说明。-pulse interference with the interference of fuzzy manipulator control methods. Using neural network training, the T-S fuzzy controller, control effectiveness. Only Simulation Module, there is no written statement. Platform: |
Size: 7168 |
Author:郑军 |
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Description: 遗传bp神经网络在pid控制器中的应用,多多指教-genetic bp pid neural network controller in the application of the exhibitions! ! Platform: |
Size: 84992 |
Author:angel |
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Description: 这是一个BP神经网络的基本程序，隐藏层数可以任意设置。-This is a Neural Network basic procedures, hidden layers can be set arbitrarily. Platform: |
Size: 1024 |
Author:贾雷 |
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Description: 这个程序采用BP神经网络对传统的PID控制器的三个参数进行整定-This procedure using BP neural network to the traditional PID controller tuning the three parameters Platform: |
Size: 1024 |
Author:周凯 |
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Description: 本文讨论了神经网络PID控制策略，提出了一种单神经元自适应PID控制器，给出了控制模型，探讨了单神经元自适应PID控制学习算法，通过修改神经元控制器连接加权系数 ，构成了自适应PID控制器。利用神经网络的自学习能力进行PID控制参数的在线整定，并使用了MATLAB软件进行了仿真研究。比较传统PID控制器与单神经元自适应PID控制器两者的仿真结果表明，神经网络PID控制器参数调节简单，具有很高的精度和很强的适应性，可以获得满意的控制效果。-This paper discusses the nerve network PID control strategy and a single neural and adaptive PID controller is presented, and it s model is given here. The algorithm of PID controller is explored by revising the weighted coefficient . Make use of the study ability of the nerve network to turing the PID control parameter, and proceeds the simulation research using matlab software. From the simulation results, it is can be shown that Neural Network PID controller have the higher accuracy and stronger adaptability, and can get satisfied control result.
Key Words: PID；single Neuron；self-adaptive
Platform: |
Size: 7168 |
Author:大同小异 |
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Description: 为了提高三级倒立摆系统控制的响应速度和稳定性,在设计Mamdani 型模糊推理规则控制器控制倒立摆系统稳定的基础上,
设计了一种更有效率的基于Sugeno 型模糊推理规则的模糊神经网络控制器。该控制器使用BP 神经网络和最小二乘法的混
合算法进行参数训练,能够准确归纳输入输出量的模糊隶属度函数和模糊逻辑规则。通过与Mamdani 型控制器的仿真对比,
表明该Sugeno 型模糊神经网络控制器对三级倒立摆系统的控制具有良好的稳定性和快速性,以及较高的控制精度。-In order to improve the three-level control of inverted pendulum system response speed and stability, in the design of Mamdani-type fuzzy inference rules of the system controller to control the stability of inverted pendulum on the basis of a more efficient design based on Sugeno-type fuzzy inference rules of fuzzy neural network controller. The controller is the use of BP neural network and hybrid least squares training algorithm parameters can be accurately summed up the amount of input and output fuzzy membership function and fuzzy logic rules. Mamdani-type controller with a simulation comparison shows that the Sugeno-type fuzzy neural network controller for the three-tier control of inverted pendulum system with good stability and fast, as well as a higher control precision. Platform: |
Size: 551936 |
Author:月到风来AA |
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Description: These set of programs are written to design a robust power system stabilizer for minimizing the effects of low frequency oscillations in electric power systems. A complete nonlinear model of the power system represented by a single machine connected to an infinite bus is developed in the Simulink environment. A fuzzy Power System Stabilizer is designed using the fuzzy logic toolbox of matlab and its parameters are tuned by a NEural Network controller. Platform: |
Size: 15360 |
Author:al-amin |
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Description: Abstract-This paper introduces the new concept of Artificial
Neural Networks (ANNs) in estimating speed and
controlling the separately excited DC motor. The neural
control scheme consists of two parts. One is the neural
estimator, which is used to estimate the motor speed. The
other is the neural controller, which is used to generate a
control signal for a converter. These two networks are
trained by Levenberg-Marquardt back propagation
algorithm. Standard three layer feed forward neural
network with sigmoid activation functions in the input and
hidden layers and purelin in the output layer is used.
Simulation results are presented to demonstrate the
effectiveness and advantage of the control system of DC
motor with ANNs in comparison with the conventional
control scheme. Platform: |
Size: 490496 |
Author:amidi |
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