Training an RBFNN using PSO gives same output for any input

I have setup an RBFNN using

net = network(1,2,[1;1],[1;0],[0 0;1 0],[0 1])

Then, I have copied the essentials from newrb such as:

net.inputs{1}.size = R;
net.layers{1}.size = Q;
net.inputWeights{1,1}.weightFcn = 'dist';
net.layers{1}.netInputFcn = 'netprod';
net.layers{1}.transferFcn = 'radbas';
net.layers{2}.size = S;
net.outputs{2}.exampleOutput = T;
net = init(net);

At last, I have assigned (assumed) optimum weights (through PSO) to this network 'net' using this:

net.b{1} = b1;
net.iw{1,1} = w1;
net.b{2} = b2;
net.lw{2,1} = w2;

Problem: When I use this network 'net' like this:

Y=net(Input)

Y contains same entries irrespective of the input. I have noticed that each entry of Y is equal to net.b{2}, which is the output bias.

For example:

Y= 6.1397       6.1397       6.1397       6.1397       6.1397.....

What's wrong here?