[1]程显毅,陈小波.基于多Agent的模式识别框[J].智能系统学报,2006,1(2):89-93.
CHENG Xian-yi,CHEN Xiao-bo.Frame of pattern recognition based on multi-Agent[J].CAAI Transactions on Intelligent Systems,2006,1(2):89-93.
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CHENG Xian-yi,CHEN Xiao-bo.Frame of pattern recognition based on multi-Agent[J].CAAI Transactions on Intelligent Systems,2006,1(2):89-93.
基于多Agent的模式识别框
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
1
期数:
2006年第2期
页码:
89-93
栏目:
学术论文—机器感知与模式识别
出版日期:
2006-10-25
- Title:
- Frame of pattern recognition based on multi-Agent
- 文章编号:
- 1673-4785(2006)02-0089-05
- Keywords:
- pattern recognition; Agent; APRF; pattern memory; pat tern emerge
- 分类号:
- TP391.4
- 文献标志码:
- A
- 摘要:
- 传统的模式识别方法主要存在两个方面的不足：集中式控制；依赖于特征抽取和选择的质量.本文的研究是在分析自上而下模式识别方法和自下而上模式识别方法的特性和优缺点基础上,基于多agent理论和技术,提出了将两种不同类型的方法集成的模式识别框架APR F（AgentBased Pattern Recognition Frame）：先用自下而上定量计算方法对模式建模, 使其有利于分类；再用自上而下的定性分析方法对模式涌现.目的是让计算机模式识别更符合人的认知过程,APRF的基本思想是：模式识别=模式建模+模式涌现.
- Abstract:
- Traditional pattern recognition methods have two disadvantages in gene ral: centralized control and depending on the quality of the features extracted or selected. APRF(AgentBased Pattern Recognition Frame) is proposed based on multiagent theories and techniques after the analysis of the cha r acteristics, advantages and disadvantages of the topdown and bottomup method s. The pattern is modeled by quantitatively computing from bottom to top so as to ease the classification. Then the pattern is emerged by the qualitatively analy tical approach from top to bottom. APRF is aimed to make the pattern recognition by computer more accordant with the cognition of human. The core of APRF is: Pa ttern Recognition=Pattern Modeling+Pattern Emergence.
- 参考文献/References:
-
［1］ JIAN A K, DUIN R P W,MAO Jianchang. Statistical pattern recognition: a view ［J］.IEEE Transaction on Pattern Analysis and Machine Intelligence，2000，22( 1 ):34-37.
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［3］王首觉.仿生模式识别——一种模式识别新模型的理论与应用［J］.电子学报，2002,30 (10):1417-1420.
?WANG Shoujue.Bionic(Topological)pattern recognition—a new model of pattern re cognition theory and its applications［J］.Acta Electronica Sinica, 2002,30(10): 1417-1420.
?［4］CHARTIER S, BOUKADOUM M. A sequential dynamic heteroassociative m e mory for multistep pattern recognition and onetomany association［J］. IEEE Transactions on Neural Networks,2006，17(1):59-68.
［5］RAHTU E, SALO M, HEIKKILA J. Affine invariant pattern recognition us ing multiscale autoconvolution［J］. IEEE Transactions on Machine Intelligence,2 005，27(6):908-918.
［6］程显毅. Agent计算［M］. 哈尔滨：黑龙江科学技术出版社，2003.
［7］骆祥峰,高 隽,张旭东.基于信任知识库的概率模糊认知图［J］. 计算机研究与发展，2003，40(7):925〗-934.
?LUO Xiangfeng,GAO Jun, ZHANG Xudong.Probabilistic fuzzy cognitive map based on b elief knowledge data base［J］.Journal of Computer Research and Development, 200 3,40(7):925-934.
［8］HOLLAND J H. 涌现——从混沌到有序［M］. 上海:上海科技教育出版社，2001.
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备注/Memo
收稿日期：2006-02-23.
基金项目:
国家自然科学基金资助项目（60473039）；
江苏省研究生创新基金资助项目（xm0 435）;
江苏省重点科技攻关资助项目（BE2004093）
作者简介：
程显毅，男，1956年生,博士,教授,主要研究方向为模式识别、多Agent系统. 参与或主持课题多项,主要学术专著《Aagent计算》，发表论文70多篇.
E-mail:xycheng@ujs.edu.cn.
陈小波，男，1982年生，江苏大学计算机科学与通信工程学院硕士研究生,主要研究方向为模式识别,多Agent系统.
更新日期/Last Update:
2009-05-05