人工神经网络知识增殖性分析  

Knowledge Increase Ability of Artificial Neural Network

作　　者：黄华[1] 罗四维[1] 刘蕴辉[1] 李爱军[1] 

机构地区：[1]北京交通大学计算机与信息技术学院,北京100044

出　　处：《计算机研究与发展》2005年第2期224-229,共6页Journal of Computer Research and Development

基　　金：国家自然科学基金项目 (69973 0 0 2 );教育部博士点基金项目 (2 0 0 2 0 0 0 40 2 0 )

摘　　要：人工神经网络的知识增殖能力是该领域的热点和难点问题 ,具有重要的理论和实践意义 对人工神经网络的知识增殖性问题进行了较深入的探讨 ,从网络推广能力的角度分析了具有知识增殖能力的神经网络系统的结构设计问题 ,指出将多个网络个体结合在一起是实现人工神经网络增殖学习的重要方法 ,网络的自治能力在此具有重要的意义 利用具有自治能力的神经网络构建的网络群体中 ,网络个体无需改变而整体具有增殖学习能力 。Knowledge increase ability is of great importance in the field of artificial neural network(ANN), which is also an open problem and absorbs most research attentions. Such researches will promote the further development of ANNs both in theory and practice. The knowledge increase ability of ANNs is discussed in depth. Theoretical analysis is firstly made in view of generalization ability, which results in a most promising solution of multiple network approach. Knowledge increase can be realized via knowledge accumulation and inheritance between single network and the network system. The conception of autonomy is of great significance for knowledge increase ability of ANNs. An autonomous artificial neural network(AANN) model is introduced to avoid centralized confidence assignment, which enables distributed confidence assignment and makes the system extensible. An experimental system is built on AANN units to testify its feasibility and the results are encouraging.

关 键 词：人工神经网络 知识增殖 VC维数 机器学习 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]