How to initialize weight and biases for single hidden layer ?

Hello Everyone ! Please i need your help !

In order to create artificial neural networks for solar radiation prediction, I need to define architecture of my NNs, especially the number of hidden neurons because i use one hidden layer. So, i would like to use the following approach: Fix the initial conditions (weights and biases) and vary the number of hidden neurons from 1 to 20, till i find architecture that gives best performance.

My questions is : How do i use 'net = init(net)' in this code ?

x = simplefitInputs;
t = simplefitTargets;

% Choose a Training Function
% For a list of all training functions type: help nntrain
% 'trainlm' is usually fastest.
% 'trainbr' takes longer but may be better for challenging problems.
% 'trainscg' uses less memory. NFTOOL falls back to this in low memory situations.
trainFcn = 'trainlm';  % Levenberg-Marquardt

% Create a Fitting Network
hiddenLayerSize = 10;
net = fitnet(hiddenLayerSize,trainFcn);

% Setup Division of Data for Training, Validation, Testing
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;

% Train the Network
[net,tr] = train(net,x,t);

% Test the Network
y = net(x);
e = gsubtract(t,y);
performance = perform(net,t,y)

% View the Network
view(net)

If there are other approaches, do not hesitate to mention them.

Thank you.