NDLI: Fuzzy Clustering RBF Neural Network Applied to Signal Processing of the Imaging Detection

Content Provider	IEEE Xplore Digital Library
Author	Yongxue Wang Yan Shang
Copyright Year	2010
Abstract	Imaging fuze can get the geometric shape and exterior features of target in very close distance, and supply the information for target recognition, so it is help to improve the destroying efficiency to enemy targets. But because of the large amount of the information and the real-time requirement, the image of target is always distorted and incomplete. In order to recognize the encountering conditions in imaging detecting, a method based on fuzzy clustering radial basis function (RBF) neural networks is presented. The fuzzy clustering RBF neural network has more adaptive to recognize the encountering conditions. A fuzzy C-means clustering method based on minimizing the mean square error in one category is adopted to determine the RBF layer, and the grads of membership degree is used to determine their shape factors. The theoretic analysis and experimental results show that, the fuzzy clustering RBF network has more powerful generalization ability than the normal RBF network.
Starting Page	321
Ending Page	324
File Size	272974
Page Count	4
File Format	PDF
ISBN	9781424450015
e-ISBN	9781424457397
DOI	10.1109/ICMTMA.2010.35
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-03-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image recognition Adaptive systems RBF neural network Shape Target recognition Clustering methods signal processing imaging detectiont Neural networks Radial basis function networks Mean square error methods fuzzy clustering Signal processing Fuzzy neural networks
Content Type	Text
Resource Type	Article

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