PROBE:NOISE-AND-ROTATION RESISTANCE OF HOPFIELD NEURAL NETWORK IN IMAGED TRAFFIC SIGN RECALL  

作　　者：Chen Ken Yang Shoujian Celal Batur 

机构地区：[1]College of Information Science and Engineering,Ningbo University [2]College of Engineering,The University of Akron

出　　处：《Journal of Electronics(China)》2013年第2期183-189,共7页电子科学学刊（英文版）

基　　金：Supported by the Natural Science Foundation of Zhejiang Province(No.2010A610105)

摘　　要：This paper examines the noise and rotation resistance capacity of Hopfield Neural Network （HNN） given four corrupted traffic sign images. In the study, Signal-to-Noise Ratio （SNR）, recall rate and pattern complexity are defined and employed to evaluate the recall performance. The experimental results indicate that the HNN possesses significant recall capacity against the strong noise corruption, and certain restoring competence to the rotation. It is also found that combining noise with rotation does not further challenge the HNN corruption resistance capability as the noise or rotation alone does.This paper examines the noise and rotation resistance capacity of Hopfield Neural Network (HNN) given four corrupted traffic sign images. In the study, Signal-to-Noise Ratio (SNR), recall rate and pattern complexity are defined and employed to evaluate the recall performance. The experimental results indicate that the HNN possesses significant recall capacity against the strong noise corruption, and certain restoring competence to the rotation. It is also found that combining noise with rotation does not further challenge the HNN corruption resistance capability as the noise or rotation alone does.

关 键 词：Hopfield Neural Network (HNN) Traffic sign identification Pattern complexity Recall rate 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]