Centralized and distributed hypothesis testing with structured adaptive networks and perceptron-type neural networks
Stelios C. A. Thomopoulos, Ioannis N. M. Papadakis, Haralambos Sahinoglou, Nickens N. Okello · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
Two different types of adaptive networks are considered for solving the centralized and distributed hypothesis testing problem. The performance of the two different types of networks is compared under different performance 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.