New neural network architecture for the fusion of independent or dependent sensor decisions
Robert Pawlak · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1994
A new neural network architecture for binary hypothesis testing is discussed. The network can utilize results from sensors making independent or dependent decisions (as well as any combination of binary data). Furthermore, it employs a novel structure, incorporating a set of trainable threshold values but no trainable weight values. The threshold values are trained using a minimum probability of error criterion, and only one threshold is modified for each training sample. Simulation results are presented comparing the performance of the network with that of the optimal parametric detector for the case of independent sensor decisions. These results show that for independent data, the performance of the net approaches that of the optimal parametric detector.