Institute of Information Theories and Applications FOI ITHEA
Abstract:
An experimental comparison of information features used by neural network is performed. The
sensing method was used. Suboptimal classifier agreeable to the gaussian model of the training data was
used as a probe. Neural nets with architectures of perceptron and feedforward net with one hidden layer were
used. The experiments were carried out with spatial ultrasonic data, which are used for car’s passenger safety
system neural controller learning. In this paper we show that a neural network doesn’t fully make use of
gaussian components, which are first two moment coefficients of probability distribution. On the contrary, the
network can find more complicated regularities inside data vectors and thus shows better results than
suboptimal classifier. The parallel connection of suboptimal classifier improves work of modular neural
network whereas its connection to the network input improves the specialization effect during training.