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[1]许少华,李盼池,何新贵.一种概率过程神经元网络模型及分类算法[J].智能系统学报,2009,4(4):283-287.
　XU Shao-hua,LI Pan-chi,HE Xin-gui.Combined probabilistic process neural network and classification algorithm[J].CAAI Transactions on Intelligent Systems,2009,4(4):283-287.

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一种概率过程神经元网络模型及分类算法

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《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 4 期数: 2009年第4期页码: 283-287 栏目: 学术论文—机器学习出版日期: 2009-08-25

Title:: Combined probabilistic process neural network and classification algorithm

文章编号:: 1673-4785(2009)04-0283-05

作者:: 许少华^1,2，李盼池¹，何新贵²; 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

Author(s):: XU Shao-hua^1,2, LI Pan-chi¹, HE Xin-gui^2; 1. School of Computer and Information Technology, Daqing Petroleum Institute, Daqing 163318, China;
2. School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China

关键词:: 动态信号分类; 贝叶斯规则; 概率过程神经元网络

Keywords:: dynamic signal classification; Bayesian rules; probabilistic process neural networks

分类号:: TP183

文献标志码:: A

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.

Abstract:: 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.

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备注/Memo

收稿日期：2009-07-16.
基金项目：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）.
通信作者：许少华.E-mail: xush62@163. com.
作者简介：
许少华, 男, 1962 年生, 博士, 教授, 博士生导师. 主要研究方向为模式识别、神经网络、智能信息处理. 在国内外学术期刊发表学术论文50余篇, 其中被SCI、EI检索20余篇.
李盼池, 男, 1969年生, 博士, 副教授, 主要研究方向为量子计算、智能优化算法及其在智能控制、智能信息处理等方面的应用. 在国内外学术期刊发表学术论文30余篇, 其中被SCI、EI 检索10余篇.
何新贵, 男, 1938年生, 教授, 博士生导师, 中国工程院院士, 北京计算机学会理事长,《计算机学报》副主编.主要研究方向为模糊逻辑、神经网络、进化计算、数据库理论, 发表学术论文140 余篇, 其中多篇被SCI、EI 检索.

更新日期/Last Update: 2009-11-16

一种概率过程神经元网络模型及分类算法 PDF下载HTML

备注/Memo

一种概率过程神经元网络模型及分类算法

PDF下载 HTML