Determining the number of hidden units in multi-layer perceptrons using F-ratios

The hidden units in multi-layer perceptrons are believed to act as feature extractors. In other words, the outputs of the hidden units represent the features in a more traditional statistical classification paradigm. This viewpoint offers a statistical, objective approach to determining the optimal number of hidden units required. This approach is based on an F-ratio test, and proceeds in an iterative fashion. The method and its application to simulated time-series data are presented.