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It is showing error tht switch expression must be a scalar or a character vector.

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% Step 1: Load the data
data = [
1 400 40 1 35.32 23.12
2 400 40 2 30.41 21.37
3 400 40 3 41.53 20.61
4 400 60 1 26.81 21.83
5 400 60 2 33.74 22.58
6 400 60 3 27.25 24.92
7 400 100 1 28.37 22.06
8 400 60 2 31.58 20.87
9 400 100 3 37.08 21.03
10 700 40 1 43.27 16.59
11 700 40 2 40.51 16.88
12 700 40 3 37.97 13.61
13 700 60 1 47.33 12.96
14 700 60 2 67.39 10.08
15 700 60 3 63.83 11.64
16 700 100 1 78.30 9.68
17 700 100 2 81.36 9.24
18 700 100 3 81.09 9.98
19 1000 40 1 83.61 8.41
20 1000 40 2 89.48 7.83
21 1000 40 3 88.07 8.06
22 1000 60 1 74.38 6.85
23 1000 60 2 76.33 6.97
24 1000 60 3 71.06 7.56
25 1000 100 1 69.34 7.06
26 1000 100 2 70.18 6.81
27 1000 100 3 73.54 7.08
28 1300 40 1 89.32 6.53
29 1300 40 2 91.68 5.86
30 1300 40 3 90.22 5.98
31 1300 60 1 88.96 5.07
32 1300 60 2 92.94 5.06
33 1300 60 3 91.87 6.07
34 1300 100 1 78.39 6.43
35 1300 100 2 81.34 5.11
36 1300 100 3 76.73 5.36
];
inputs = data(:, 2:4)';
targets = data(:, 5:6)';
% Step 2: Define the neural network architecture
hiddenLayerSize = 10; % Choose the number of neurons in the hidden layer
net = feedforwardnet(hiddenLayerSize);
% Step 3: Define the genetic algorithm parameters
ga_opts = gaoptimset('Display', 'iter', 'PopulationSize', 50, 'Generations', 100);
% Step 4: Train the hybrid model
[net_ga, ~] = ga(@(x)train_neural_network(x, inputs, targets), net.numWeights, ga_opts);
% Step 5: Test the model
outputs = net_ga(inputs);
% Step 6: Evaluate the model's performance
% You can evaluate the performance using various metrics such as RMSE, MAE, etc.
% Define a function to train the neural network
function loss = train_neural_network(weights, inputs, targets)
net = feedforwardnet(hiddenLayerSize);
net = setwb(net, weights);
net = train(net, inputs, targets);
outputs = net(inputs);
loss = mse(outputs - targets);
end

Akzeptierte Antwort

Walter Roberson
Walter Roberson am 7 Apr. 2024
[net_ga, ~] = ga(@(x)train_neural_network(x, inputs, targets, hiddenLayerSize), net.numWeightElements, ga_opts);
You cannot pass ga options directly after the number of variables. The options are positional. You need to use either
x = ga(fun,nvars,A,b,Aeq,beq,lb,ub,nonlcon,options)
or
x = ga(fun,nvars,A,b,Aeq,beq,lb,ub,nonlcon,intcon,options)

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