1、

Probabilistic neural network ( PNN) is a classification network, which is based on Bayesian decision theory and probability function estimation theory.

F.Specht提出的概率神经网络(Probabilistic Neural Network,PNN)是基于密度函数估计和贝叶斯决策理论而建立的一种分类网络。

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2、

The algorithm is based on Bayesian decision machinery, with estimation of the structure from probability density function, for two-class problems. ( 3) The paper gives out a semi-supervised learning algorithm based on FCM.

该算法基于贝叶斯决策理论,通过概率密度函数进行分布估计,对两类别半监督学习问题进行判定。(3)给出了一个基于FCM的半监督学习算法。

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3、

At first, the basic probability assignment is established; then, the trust function is calculated; at last, the decision is made according to some combination rule.

首先建立基本概率赋值,然后计算信任函数,最后按照一定的组合规则作出决策。

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4、

Based on the target BPAF ( basic probability assignment function) in D-S evidence theory, the target characteristics fusion problems in the decision layers had been studied.

在D-S证据理论框架下,研究了基于目标基本概率赋值(BPAF)判决的决策层目标属性融合问题。

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5、

A few concepts of D-S evidence theory and D-S evidence combination rule and decision methods are presented and the program of D-S evidence combination rule on Basic Probability Assignment Function ( BPAF) is described. The Combination of Evidences in an Incomplete Frame of Discernment

本文在简要介绍D-S证据组合理论框架基础上,阐述了基于D-S证据组合理论的信息融合一般步骤及决策层判决方法;不完备识别框架下的证据组合方法

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