Feed-forward neural networks for shower recognition: construction and generalization

Strictly layered feed-forward neural networks are explored as recognition tools for energy deposition patterns in a calorimeter. This study is motivated by possible applications for on-line event selection. Networks consisting of linear threshold units are generated by a constructive learning algorithm, the Patch algorithm. As a non-constructive counterpart the back-propagation algorithm is applied. This algorithm makes use of analogue neurons. The generalization capabilities of the neural networks resulting from both methods are compared to those of nearest-neighbour classifiers and of Probabilistic Neural Networks implementing Parzen-windows. The latter non-parametric statistical method is applied to estimate the optimal Bayesian classifier. For all methods the generalization capabilities are determined for different ways of pre-processing of the input data. The complexity of the feed-forward neural networks studied does not grow with the training set size. This favours a hardwired implementation of these neural networks as any implementation of the other two methods grows linearly with the training set size.