does anybody ever use neural network to do a prediction

Great approach! Using Bayesian Regularization (`trainbr`) and Mean Squared Error with Regularization (`msereg`) is an excellent strategy to prevent overfitting when training neural networks. Here are the steps to ensure your process is on point:

1. Preprocess Data:
   - Normalize your data to be within the range `[-1, 1]` as you mentioned. This helps in better convergence during training.

2. Create and Configure the Neural Network:
   - Create a feedforward neural network and configure it to use the `trainbr` training function and `msereg` performance function.

3. Split Data:
   - Divide your dataset into training (70%) and testing (30%) sets randomly.

4. Train the Network:
   - Train the network with the training data using the `train` function.

5. Evaluate Performance:
   - Evaluate the network's performance using the testing data to ensure that it generalizes well.

Here’s a concise code snippet to guide you through the process:

% Assuming 'inputData' and 'targetData' are your preprocessed datasets

% Split the data into training (70%) and testing (30%) sets
[trainInd, ~, testInd] = dividerand(size(inputData, 2), 0.7, 0, 0.3);

trainInput = inputData(:, trainInd);
trainTarget = targetData(:, trainInd);
testInput = inputData(:, testInd);
testTarget = targetData(:, testInd);

% Create and configure the neural network
hiddenLayerSize = 10;  % Adjust based on your needs
net = feedforwardnet(hiddenLayerSize, 'trainbr');
net.performFcn = 'msereg';

% Train the network
[net, tr] = train(net, trainInput, trainTarget);

% Evaluate the network on test data
predictions = net(testInput);

% Calculate performance on test data
performance = perform(net, testTarget, predictions);

disp(['Performance on test data: ', num2str(performance)]);

 Key Points:
- Regularization: By using `trainbr`, you're automatically applying Bayesian regularization, which helps control the complexity of the network and reduces the risk of overfitting.
- Performance Evaluation: Evaluate your network on unseen data (test set) to ensure it generalizes well and does not overfit the training data.

This should set you on the right path to training a robust neural network for your prediction task.