uppernet.m

ANN基本知识和应用课件

Sample = [

 15,   15,   15,   15,   15,   15,   15,   15,   25,   25,   25,   25,   25,   25,   25,   25,   25,   25,   25,   35,   35,   35,   35,   35,   35,   35,   35,   35,   35,   35,   35,   35,   45,   45,   45,   45,   45,   45,   45,   45,   45,   45,   45,   45,   45,   45;

  5,    5,    5,    5,   15,   15,   15,   15,    5,    5,    5,    5,   15,   15,   15,   25,   25,   25,   25,    5,    5,    5,   15,   15,   15,   15,   25,   25,   25,   35,   35,   35,    5,    5,    5,    5,   15,   15,   15,   15,   25,   25,   25,   25,   35,   35;

0.5,  1.0,  1.5,  2.5,  0.5,  1.0,  1.5,  2.5,  0.5,  1.0,  1.5,  2.5,  0.5,  1.0,  1.5,  0.5,  1.0,  1.5,  2.5,  1.0,  1.5,  2.5,  0.5,  1.0,  1.5,  2.5,  0.5,  1.0,  2.5,  1.0,  1.5,  2.5,  0.5,  1.0,  1.5,  2.5,  0.5,  1.0,  1.5,  2.5,  0.5,  1.0,  1.5,  2.5,  1.5,  2.5

];

Target = [

5.35, 4.96, 4.61, 4.09, 5.98, 7.25, 7.77, 7.75, 3.19, 3.06, 2.95, 2.76, 3.67, 4.18, 4.88, 2.94, 3.96, 4.38, 4.27, 2.23, 2.16, 2.07, 2.61, 2.72, 2.71, 2.51, 2.48, 2.88, 3.81, 2.71, 3.03, 3.32, 1.79, 1.76, 1.72, 1.66, 5.22, 5.68, 6.04, 6.26, 2.03, 2.29, 2.51, 3.18, 2.51, 2.89

];

Test = [25,35,35,45;15,5,25,35;2.5,0.5,1.5,1.0];

Goal = [4.58,2.30,3.23,2.21];

Upper = network;

Upper.numInputs = 1;
Upper.numLayers = 3;

Upper.biasConnect = [1;1;1];
Upper.inputConnect = [1;0;0];
Upper.layerConnect = [0,0,0;1,0,0;0,1,0];
Upper.outputConnect = [0,0,1];
Upper.targetConnect = [0,0,1];

Upper.inputs{1}.size = 3;
Upper.inputs{1}.range = [15,45;5,35;0.5,2.5];

Upper.layers{1}.size = 7;
Upper.layers{1}.transferFcn = 'tansig';
Upper.layers{1}.initFcn = 'initnw';

Upper.layers{2}.size = 5;
Upper.layers{2}.transferFcn = 'tansig';
Upper.layers{2}.initFcn = 'initnw';

Upper.layers{3}.size = 1;
Upper.layers{3}.transferFcn = 'purelin';
Upper.layers{3}.initFcn = 'initnw';

Upper.initFcn = 'initlay';
Upper.performFcn = 'mse';
Upper.trainFcn = 'trainlm';

Upper.trainParam.epochs = 1e+6;
Upper.trainParam.goal = 5e-3;
Upper.trainParam.lr = 1e-3;

Upper = init(Upper);

Upper = train(Upper,Sample,Target);

Out = sim(Upper,Test);