基于RBF神经网络的集成增量学习算法  

RESEARCH ON RBF NEURAL NETWORK-BASED ENSEMBLE INCREMENTAL LEARNING ALGORITHM

作　　者：彭玉青[1] 赵翠翠[1] 高晴晴[1] Peng Yuqing;Zhao C uicui;Gao Qingqing(School of Computer Science and Engineering, Hebei University of Technology, Tianjin 300401 , China)

机构地区：[1]河北工业大学计算机科学与软件学院,天津300401

出　　处：《计算机应用与软件》2016年第6期246-250,共5页Computer Applications and Software

基　　金：国家自然科学基金项目(51175145);天津市自然科学基金项目(13JCYBJC15400);河北省高等学校科学技术研究项目(ZD2014030)

摘　　要：针对增量学习的遗忘性问题和集成增量学习的网络增长过快问题,提出基于径向基神经网络(RBF)的集成增量学习方法。为了避免网络的遗忘性,每次学习新类别知识时都训练一个RBF神经网络,把新训练的RBF神经网络加入到集成系统中,从而组建成一个大的神经网络系统。分别采用最近中心法、最大概率法、最近中心与最大概率相结合的方法进行确定获胜子网络,最终结果由获胜子网络进行输出。在最大概率法中引入自组织映(SOM)的原型向量来解决类中心相近问题。为了验证网络的增量学习,用UCI机器学习库中Statlog(Landsat Satellite)数据集做实验,结果显示该网络在学习新类别知识后,既获得了新类别的知识也没有遗忘已学知识。Aiming at the forgetfulness problem of incremental learning and the excessive network growth problem of the integrated incremental learning,this paper proposes an integrated incremental learning method which is based on the radial basis function (RBF)neural network.In order to avoid the forgetfulness of the network,for each knowledge learning of new category we all trained an RBF neural network,and added the newly trained RBF neural network to the integrated system so as to form a large system of neural networks.To determine the winning sub-network,we adopted the nearest centre method,the maximum probability method and the combination of these two methods,and the final result was outputted by the winning sub-network.Moreover,we introduced the prototype vectors of self-organising map to maximum probability method for solving the problem of class centre similarity.For verifying the proposed network incremental learning,we made the experiments using the Statlog (Landsat Satellite)dataset in UCI machine learning library.Experimental results showed that after learning the knowledge of new category,this network could accept the new without forgetting the learned knowledge.

关 键 词：RBF SOM 增量学习 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]