摘要
结合动态模糊神经网络和补偿模糊神经网络,提出一种改进的动态模糊神经网络。首先介绍动态补偿模糊神经网络的结构和学习算法,然后将其用于人脸识别。对Weizmann人脸数据库和ORL人脸数据库的人脸图像识别实验表明,动态补偿模糊神经网络分类器算法性能优于一般的动态模糊神经网络。
In this paper,an improved Dynamic Fuzzy Neural Network is proposed by combining Dynamic Fuzzy Neural Network and Compensatory Fuzzy Neural Network.At first,the structure and learning algorithm of the Dynamic Compensatory Fuzzy Neural Network,abbr.DCFNN,is introduced,next they are applied to face recognition.Face image recognition experiments on Weizmann face database and ORL face database show that the performance of DCFNN is better than that of ordinary Dynamic Fuzzy Neural Networks.
出处
《计算机应用与软件》
CSCD
北大核心
2012年第1期56-59,共4页
Computer Applications and Software
基金
国家自然科学基金(60572034
60973094)
教育部新世纪优秀人才计划项目(NCET-06-0487)
江苏省自然科学基金(BK2006081)
江南大学创新团队研究计划项目(JNIRT0702)
关键词
模糊神经网络
动态补偿模糊神经网络
模糊规则
人脸识别
Fuzzy neural network Dynamic compensatory fuzzy neural network Fuzzy rule Face recognition
