一种概率过程神经元网络模型及分类算法  

Combined probabilistic process neural network and classification algorithm

作　　者：许少华[1,2] 李盼池[1] 何新贵[2] 

机构地区：[1]大庆石油学院计算机与信息技术学院,黑龙江大庆163318 [2]北京大学信息科学技术学院,北京100871

出　　处：《智能系统学报》2009年第4期283-287,共5页CAAI Transactions on Intelligent Systems

基　　金：国家自然科学基金资助项目(60572174);黑龙江省教育厅科学技术研究资助项目(11521013);黑龙江省自然科学基金资助项目(ZA2006-11);黑龙江省科技攻关资助项目(GZ07A103)

摘　　要：针对动态信号分类及与先验类别知识融合问题,提出了一种概率过程神经元网络模型.模型将贝叶斯概率分类机制与过程神经元网络动态信号处理方法相结合,通过在前馈过程神经元网络中增加一个模式单元层,以及采用归一化指数类型激励函数,实现基于贝叶斯规则的动态信号分类.分析了概率过程神经元网络分类机制与贝叶斯分类规则的等价性,给出了具体的学习算法,实验结果验证了模型和算法的有效性.A probabilistic process neural network has been proposed in order to provide integration of a priori knowledge with dynamic information classification. In this model, Bayesian classification was combined with the dynamic information processing of process neural networks. Dynamic information classification based on Bayesian rules was realized by adding a pattern neuron layer and a summing neuron layer to a feed forward process neural network and applying the normalized exponential activation function to the hidden layer. Classification equivalence between probabilistic process neural networks and Bayesian rules was analyzed and a concrete learning algorithm presented. Experimental results showed the effectiveness of the proposed model and algorithm.

关 键 词：动态信号分类 贝叶斯规则 概率过程神经元网络 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]