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⛄ 内容介绍
神经网络是一种黑箱建模方法,具有很高的非线性映射能力.研究了基于神经网络的液压系统动态模型建模方法.首先建立液压系统的传递函数模型,通过该模型产生样本数据,以液压系统的输入压强,节流阀截面积及四通阀控制信号为输入,液压缸压强为输出;构建NARX神经网络,建立液压系统动态模型.经过与系统的传递函数模型的输入输出进行对比,证明采用NARX神经网络建立动态模型的方法是可行的.与RNN神经网络模型进行对比,证明NARX神经网络在建立液压系统动态模型方面更具有优越性.
⛄ 代码
trainFcn = 'trainlm'; % Levenberg-Marquardt backpropagation.% Create a Nonlinear Autoregressive Network with External InputinputDelays = 1:5;feedbackDelays = 1:5;hiddenLayerSize = 10;net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize,'open',trainFcn);% Prepare the Data for Training and Simulation% The function PREPARETS prepares timeseries data for a particular network,% shifting time by the minimum amount to fill input states and layer% states. Using PREPARETS allows you to keep your original time series data% unchanged, while easily customizing it for networks with differing% numbers of delays, with open loop or closed loop feedback modes.[x,xi,ai,t] = preparets(net,X,{},T);% Setup Division of Data for Training, Validation, Testingnet.divideParam.trainRatio = 70/100;net.divideParam.valRatio = 15/100;net.divideParam.testRatio = 15/100;% Train the Network[net,tr] = train(net,x,t,xi,ai);% save('net.mat',net)% Test the Networky = net(x,xi,ai);e = gsubtract(t,y);performance = perform(net,t,y)% View the Networkview(net)
⛄ 运行结果
⛄ 参考文献
[1] Zhan L , Hayashibe M , Qin Z , et al. FES-Induced Muscular Torque Prediction with Evoked EMG Synthesized by NARX-Type Recurrent Neural Network[C]// IEEE/RSJ International Conference on Intelligent Robots & Systems. IEEE, 2012.
[2] Dipietro R , Rupprecht C , Navab N , et al. Analyzing and Exploiting NARX Recurrent Neural Networks for Long-Term Dependencies[J]. 2017.
[3] 許庭偉. 應用ＮＡＲＸ－ＲＮＮ學習法則模擬壓電致動器之磁滯模型. 2006.
[4] 李岩, 何周. 基于MATLAB GUI的光伏发电预测平台设计[J]. 电器与能效管理技术, 2016, 000(024):49-53.
