<title>Centralized and distributed hypothesis testing with structured adaptive networks and perceptron-type neural networks</title>

Abstract

ABSTRACT Two different types of adaptive networks are considered for solving the centralized and distributed hy-pothesis testing problem. The performance of the two different types of networks is compared under differentperformance indices and training rules. It is shown that training rules based on the Neyman-Pearson criterion outperform error based training rules. Simulations are provided for data that are linearly and nonlinearly separable. I. INTRODUCTION The optimum Bayesian and Neyman-Pearson solution to the distributed decision fusion problem bearsstriking similarities to the structure of a neural network (NN), [28,29]. Moreover, NNs can, in principle learn arbitrary input-output mappings, provided that they are sufficiently smooth. These two facts motivate theuse of NNs for solving the centralized and distributed hypothesis testing problem. In selecting the properNN layout, one could argue that a perceptron-type NN can learn any input-output mapping, thus it can betrained to solve the hypothesis testing problem. However, the ability of a perceptron-type NN to learn an