neural network for predict bluer coefficient in matlab

To train a neural network for predicting Bluer coefficient in Matlab, you will need to follow these steps:

1. Prepare your data: Collect a dataset of sample inputs and outputs. In this case, you will need to collect data on the features that influence Bluer coefficient and the corresponding Bluer coefficient values. You will need to split the dataset into training, validation, and testing sets.

2. Define your neural network: Use Matlab's patternnet function to define a neural network. The patternnet function is typically used for regression problems such as this one.

3. Configure your neural network: Use Matlab's configure function to specify parameters for training the network such as the number of hidden layers, learning rate, and training algorithm.

4. Train your neural network: Use the train function to train your neural network on the training dataset. Use the validation dataset to check the performance of the network and prevent overfitting.

5. Test the performance of the network: Use the testing dataset to evaluate the performance of the network on unseen data. Compare the predicted Bluer coefficient values with the actual values to determine the accuracy of the network.

Here's an example code to get you started:

main.m
% Load data
load bluer_data.mat

% Prepare data
inputs = predictors;
targets = response;

% Split data into training, validation, and testing sets
[trainInd, valInd, testInd] = divideblock(size(targets,2), 0.6, 0.2, 0.2);

% Define neural network
net = patternnet(10); % 10 hidden layers

% Configure neural network
net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};
net.outputs{2}.processFcns = {'removeconstantrows','mapminmax'};
net.divideFcn = 'divideblock';
net.divideParam.trainInd = trainInd;
net.divideParam.valInd = valInd;
net.divideParam.testInd = testInd;
net.trainFcn = 'trainlm';
net.performFcn = 'mse';
net.trainParam.goal = 0.001;
net.trainParam.showWindow = 0;

% Train neural network
[net,~] = train(net,inputs,targets);

% Test performance of neural network
outputs = net(inputs(:,testInd));
perf = perform(net,targets(:,testInd),outputs)
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