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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 algorit
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:针对能够采用仿射非线性表示的含有未建模动态的SISO非线性系统,讨论了一种基于神经网络的自适应
控制方法.该方法对受控对象的已知部分.采用反馈线性化方法设计控制器,用神经网络在线补偿未建模动态及
外部干扰等引起的误差,从而实现自适应控制。对具有未建模动态的双车倒立摆设计了输出反馈自适应控制系
统.仿真表明该方法是有效的。 -A discussion is devoted to design neural network adaptive control scheme of t
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永磁同步电机神经网络自适应滑模控制器设计Permanent magnet synchronous motor using neural network adaptive sliding mode controller design-Permanent magnet synchronous motor using neural network adaptive sliding mode controller design
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Neural Network Modeling of a Tuned PID Controller
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BP神经网络设计自适应逆控制器,实现无模型控制-BP neural network design adaptive inverse controller, model control
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用BP神经网络优化模糊控制器,进行学习,系统有两个输入,输出误差曲线和期望曲线,BP学习误差曲线-BP neural network to optimize the fuzzy controller learning system has two inputs, the output error curve and expectations of the curve, the BP learning error curve
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倒立摆神经网络模型参考控制器设计,过程很繁琐,里面包括参考的模型,实际的倒立摆模型-Inverted pendulum neural network model reference controller design process is cumbersome, which includes the reference model, the actual inverted pendulum model
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用RBF神经网络优化PID控制器,可以直接使用-Optimize PID controller based on RBF neural network
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针对G-C时滞神经网络,设计了一个滑模控制器,并利用这个控制器使两个神经网络达到同步。用matlab进行仿真。-GC Delay Neural Networks, a sliding mode controller is designed, and the controller so that the two neural network to achieve synchronization. Simulation using matlab.
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利用模糊神经网络自动建立驾驶意图识别模糊控制器-built driver intention recognition fuzzy controller by fuzzy neural network
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神经网络,神经自校正控制器的matlab仿真,神经网络控制与matlab仿。反馈线性化控制。-Neural networks, neural self-tuning controller matlab simulation, neural network control with Matlab simulation. Feedback linearization control.
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bp神经网络控制PID控制器 训练1000次得到一个更为仔细的值-bp neural network control PID controller training 1000 times to get a more detailed value
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基于径向基(rbf)神经网络的预测控制器设计中对神经网络进行聚类分析-Based on radial basis function (RBF) cluster analysis to the neural network forecast controller design of neural networks
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采用基于BP神经网络的PID控制方法,设计PID控制器的在线调整控制系统,改善系统的动态性能-Based on BP neural network PID control, PID controller design line adjustment control system, improve the system dynamic performance
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单神经元神经网络PID控制器设计程序实例-Single neuron neural network PID controller design process instance
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程序1:遗传算法和非线性规划函数的优化;
程序2:基于遗传算法的BP神经网络优化;
程序3:基于遗传算法的TSP算法;
程序4:基于遗传算法的LQR控制器优化设计
程序5:基于遗传算法的函数优化-Program 1: genetic algorithms and nonlinear programming function optimization Program 2: Based on the genetic algorithm BP neural network optim
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In this paper a Conventional PD Controller and Fuzzy Neural Controller for trajectory tracking of mobile robot
platform is presented. Using computed torque method is given input- torque on mobile robot wheels. The
locomotion control structure is
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采用遗传算法训练具有7个隶属函数的FNN控制器权值的一个好用程序,读自MATLAB工具箱的神经网络理论与应用程序源代码一书。-A useful training program using genetic algorithms with seven membership function FNN controller weights, read from MATLAB toolbox neural network theory and application source code book.
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模拟人类实际神经网络的数学方法问世以来,人们已慢慢习惯了把这种人工神经网络直接称为神经网络。神经网络在系统辨识、模式识别、智能控制等领域有着广泛而吸引人的前景,特别在智能控制中,人们对神经网络的自学习功能尤其感兴趣,并且把神经网络这一重要特点看作是解决自动控制中控制器适应能力这个难题的关键钥匙之一。
-Since the actual neural network simulation of human mathematical methods available, it has grad
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基于S函数BP神经网络的PID控制器的研究与学习,方便简单,实用性特别强。-S function based on BP neural network PID controller research and study, convenient and simple, practical particularly strong.
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