控制理论与应用2002，Vol.19Issue(3)：435-437,441,4.
Neyman-Person准则的神经网络实现新算法
New Neural Network Realization Algorithm for Neyman-Person Criterion
王祁 1沈国峰 1张兆礼1
作者信息
- 1. 哈尔滨工业大学自动化测试与控制系,哈尔滨,150001
- 折叠
摘要
Abstract
Neyman-Person criterion in hypothesis testing is a method based on the probability rate for problems like classificaton, 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.关键词
神经网络/数据融合/假设检验/Neyman-Person准则Key words
neural network/data fusion/hypothesis testing/Neyman-Person criterion分类
信息技术与安全科学引用本文复制引用
王祁,沈国峰,张兆礼..Neyman-Person准则的神经网络实现新算法[J].控制理论与应用,2002,19(3):435-437,441,4.
