New Neural Network Realization Algorithm for Neyman-Person Criterion
Ney man-Person准则的神经网络实现新算法（英文）

Neyman-Person criterion in hypothesis testing is a method based on the probability rate for problems like classification, detection, and pattern recognition. Solutions through neural network to those problems would be very desirable. However, the traditional least square learning algorithms, like backpropagation, provide no guarantee for success. This paper intends to improve a kind of non-least-square learning algorithm, decide the criterion of the probability distribution and give a better algorithm based on the absolute error. Aside from theoretical argument,the proposed algorithm is examined on a simulated problem and compared with other algorithms. The simulative result proves that the new algorithm has fewer errors and is more suitable for the Neyman-Person criterion.