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

% 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

Answer 1

[Walter Roberson]

[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)