CAJ | 학술논문

假设检验中Neyman-Person准则是一种基于似然比的信号分类、检测、识别方法.神经网络是实现这种判定准则的优选方案,但是传统的最小平方学习算法,如BP算法等,往往不能取得全局最优解.本文针对一种非最小平方学习算法,提出了一种概率分配原则,并给出了一种Neyman-Person准则的神经网络实现新算法.文中对新算法在假设检验中的应用进行了仿真验证,结果表明新算法具有更小的误差,更加适用于Neyman-Person准则.
가설검험중Neyman-Person준칙시일충기우사연비적신호분류、검측、식별방법.신경망락시실현저충판정준칙적우선방안,단시전통적최소평방학습산법,여BP산법등,왕왕불능취득전국최우해.본문침대일충비최소평방학습산법,제출료일충개솔분배원칙,병급출료일충Neyman-Person준칙적신경망락실현신산법.문중대신산법재가설검험중적응용진행료방진험증,결과표명신산법구유경소적오차,경가괄용우Neyman-Person준칙.
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.