The objective of model selection is to find the network architecture with the best generalization properties. We want to improve the final selection error obtained before (0.263 NSE). The best selection error is achieved using a model with the most appropriate complexity to produce a good data fit. Order selection algorithms are responsible for find the optimal number of perceptrons in the neural network. The following chart shows the results of the incremental order algorithm.

The objective of model selection is to find the network architecture with the best generalization properties. That is, we want to improve the final selection error obtained before (0.263 NSE). The best selection error is achieved by using a model with the most appropiate complexity to produce an adequate fit of the data. Order selection algorithms are responsible for find the optimal number of perceptrons in the neural network. The following chart shows the results of the incremental order algorithm.