摘要
对基于模糊神经网络的人脸图像分类器进行研究。将多输入单输出模糊推理系统改造成多输入多输出的模糊神经分类器,并提出了一种改进的模糊神经分类器,改进模型的计算量明显减少。在将模糊规则库与训练样本集对应的基础上提出了一种模糊隶属函数参数的初始化方法。该初始化方法的优点在于它充分利用了训练样本所包含的鉴别信息。在ORL人脸的原始图像空间中用上述方法设计分类器,获得了较好的实验结果。
A study on classifier of face images based on fuzzy neural network （FNN） was presented. A MISO（multiple-input sigle output） fuzzy inference system was modified into a MIMO （multiple-lnput multiple-output） fuzzy-neuro classifier. An improved fuzzy-neuro classifier was proposed which reduced the computational load apparently. An initialization method of parameters of fuzzy membership functions was suggested based on the correspondence between fuzzy rule base and training set. The advantage of the new initialization method lied in fully utilizing the discriminant information contained in the training set. Good experiments results were obtained using the fuzzy-neuro classifier designed in the original image space of ORL（olivertti research lab） images.
出处
《微纳电子技术》
CAS
2007年第7期465-469,共5页
Micronanoelectronic Technology
基金
国家自然科学基金(60472060
60572034)
中国科学院沈阳自动化研究所机器人学重点实验室基金(RL200108)
江苏省自然科学基金(BK2006081)
图像处理与图像通信实验室开放基金项目(KJS03038)
关键词
模式识别
神经网络
模糊系统
模糊神经网络
规则库
人脸识别
pattern recognition
neural networks
fuzzy system
fuzzy neural networks （FNN）
rule base
face recognition
