【lstm预测】基于LSTM实现人行为轨迹预测matlab代码

川长思鸟来 2022-09-17 05:20 353阅读 0赞

### 1 简介 ###

LSTM(Long Short-Term Memory)是长短期记忆网络，是一种时间递归神经网络，适合于处理和预测[时间序列][Link 1]中间隔和延迟相对较长的重要事件。

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## 2 部分代码 ##

clc;clear all;close all;
    
    %synthetic reach data from LQG 2 joint controller
    load('dat3.mat')
    load netIMtoAng.mat
    load netAngToPos.mat
    
    YPred = predict(netIMtoAng,dat3);
    Out1 = netAngToPos(YPred');
    
    %% visualise decoded data
    for i= 1:160
       % plot path in joint space
       p1 =  Out1(1:51,i);
       p2 =  Out1(52:102,i);
       v1 =  Out1(103:153,i);
       v2 =  Out1(154:204,i);
       subplot(131);plot(p1,p2,'.k'); title('Pos');hold on
       subplot(132);plot(v1,v2,'.k'); title('Vel');
       hold on;
    %     pause
       subplot(133); plot(tTra(3,i),tTra(4,i),'o'); hold on;
    end
    % axis image;
    subplot(132);xlabel('shoulder (rad)');
    ylabel('elbow (rad)');
    
    
    %% Test
    t = Tiff('M7.tif','r');
    S1   = read(t);
    s{7} = rgb2gray(S1);
    B{7} = imresize(s{7},[28 28]);
    TestImage(:,:,1,1) = B{7}(:,:);
    
    YPred = predict(netIMtoAng,TestImage(:,:,1,1));
    Out2 = netAngToPos(YPred');
    
    subplot(141); imshow(S1);title('Cartesian Work space')
    subplot(142); plot(tTra(3,7),tTra(4,7),'or'); hold on;
    plot(pi/2,pi/2,'or'); hold on;
    xlim([0.8,2.4]);ylim([0.8,2.4]);axis square;title('Joint space');
    xlabel('shoulder (rad)');
    subplot(143);
    p1 =  Out2(1:51,1);
    p2 =  Out2(52:102,1);
    v1 =  Out2(103:153,1);
    v2 =  Out2(154:204,1);
    plot(p1,p2,'.k'); title('Pos');hold on;
    xlim([0.8,2.4]);ylim([0.8,2.4]);
    xlabel('shoulder (rad)');
    ylabel('elbow (rad)');
    axis square
    subplot(144);plot(v1,v2,'.k'); title('Vel');
    xlabel('shoulder (rad/s)');
    ylabel('elbow (rad/s)');
    xlim([-2.5,2.5]);ylim([-2.5,2.5]);
    axis square
    
    %% Lstm
    data = Out1(3:51,1)';
    % dataY = Out1(54:102,1)';
    % velX = Out1(105:153,1)';
    % velY = Out1(156:204,1)';
    % DD   = [dataX; dataY; velX; velY];
    
    figure
    plot(data)
    
    %% prepare input -output for lstm
    numTimeStepsTrain = floor(0.9*numel(data));
    
    dataTrain = data(1:numTimeStepsTrain+1);
    dataTest = data(numTimeStepsTrain+1:end);
    mu       = mean(dataTrain);
    sig       = std(dataTrain);
    dataTrainStandardized = (dataTrain - mu) / sig;
    XTra = dataTrainStandardized(1:end-1);
    YTra = dataTrainStandardized(2:end);
    
    %% LSTM training
    numFeatures = 1;
    numResponses = 1;
    numHiddenUnits = 200;
    
    layers = [ ...
       sequenceInputLayer(numFeatures)
       lstmLayer(numHiddenUnits)
       fullyConnectedLayer(numResponses)
       regressionLayer];
    
    options = trainingOptions('adam', ...
       'MaxEpochs',250, ...
       'GradientThreshold',1, ...
       'InitialLearnRate',0.005, ...
       'LearnRateSchedule','piecewise', ...
       'LearnRateDropPeriod',125, ...
       'LearnRateDropFactor',0.2, ...
       'Verbose',0, ...
       'Plots','training-progress');
    
    net = trainNetwork(XTra,YTra,layers,options);
    analyzeNetwork(net)
    %% forcast- predicted trajectory
    dataTestStandardized = (dataTest - mu) / sig;
    XTest = dataTestStandardized(1:end-1);
    net = predictAndUpdateState(net,XTra);
    [net,YPred] = predictAndUpdateState(net,YTra(end));
    
    numTimeStepsTest = numel(XTest);
    for i = 2:numTimeStepsTest
      [net,YPred(:,i)] = predictAndUpdateState(net,YPred(:,i-1),'ExecutionEnvironment','cpu');
    end
    YPred = sig*YPred + mu;
    YTest = dataTest(2:end);
    rmse = sqrt(mean((YPred-YTest).^2))
    figure
    plot(dataTrain(1:end-1),'Linewidth',6)
    hold on
    idx = numTimeStepsTrain:(numTimeStepsTrain+numTimeStepsTest);
    plot(idx,[data(numTimeStepsTrain) YPred],'.-','Markersize',32)
    hold off
    legend(["Observed" "Forecast"]);
    set(gca,'Fontsize',20);
    xlabel('Time steps');
    ylabel('Shoulder angle position(rad)')

## 3 仿真结果 ##

![37d3a5190be211ca82cc40d38d3bcbc2.png][]

![e453e2fda75c02a8b6b40afa9b71f886.png][]

## 4 参考文献 ##

\[1\]刘一雄. 基于LSTM及注意力机制的行人轨迹预测模型\[D\]. 华中科技大学, 2018.

![图片][340814e01ce3f636b05309911e477dfd.png]

[Link 1]: https://baike.so.com/doc/6192108-6405360.html
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[e1e26ccd5ca7670d695b672541ec2a63.png]: /images/20220828/5fd67e623d0f4dbf9324ae25a4128633.png
[37d3a5190be211ca82cc40d38d3bcbc2.png]: /images/20220828/193058ca14eb47fca00a39e35bda89f8.png
[e453e2fda75c02a8b6b40afa9b71f886.png]: /images/20220828/521b5b6d1de0427bb7a1779fa6598b4c.png
[340814e01ce3f636b05309911e477dfd.png]: /images/20220828/012aa6c645f846a2a2243568a235a218.png