摘要
介绍一种区间小波的构造方法.并将区间小波与神经网络相结合,提出一种用于信号分类的分类区间小波网络,利用它解决小波网络的基底空间与被学习信号所属空间不匹配的问题.在分类区间小波网络模型中引入模拟退火策略,并采用自适应变学习系数训练网络.实验结果表明,将分类区间小波网络应用于雷达目标识别,可以减少神经元数目,提高网络收敛速度,并能较好解决高维学习的"维数灾难"问题,获得较好的分类效果.
A method to construct interval-wavelets is introduced in this paper, which is combined with neural network. A model of interval-wavelets neural network is proposed to classify signals, which can solve the problem that basic space does not match the space of learnt signals. Analogue annealing strategy is introduced into the model, and adaptive varied learning coefficient is applied to train the network . The experimental results show that the number of neurons can be decreased, the convergence rates also can be improved and ＂dimension disaster ＂ problem is solved with better classification effect as well when applying interval-wavelets neural network in Radar Target Recognition.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2006年第3期388-392,共5页
Pattern Recognition and Artificial Intelligence
关键词
区间小波
分类区间小波网络
目标识别
雷达
Interval-Wavelets, Classification Interval -Wavelets Network, Target Recognition, Radar
