How can the performance of a neural network vary considerably without changing any parameters?
In general, there is no guarantee that ANNs such as a multi-layer Perceptron network will converge to the global minimum squared error (MSE) solution. The final state of the network can be heavily dependent on how the network weights are initialized. Since most initialization schemes (including Nguyen-Widrow) use random numbers to generate the initial weights, it is quite possible that some initial states will result in convergence on local minima, whereas others will converge on the MSE solution.