Conference Paper

# An Improvement on PCA Algorithm for Face Recognition.

DOI: 10.1007/11427391_163 Conference: Advances in Neural Networks - ISNN 2005, Second International Symposium on Neural Networks, Chongqing, China, May 30 - June 1, 2005, Proceedings, Part I

Source: DBLP

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**ABSTRACT:**An improved Elman neural network, the hybrid feedback Elman neural network is presented for the modeling of unknown delay and high-order nonlinear system. The stability of the improved Elman network is proved in the sense of Lyapunov stability theory, and then chaos searching is imported to train it, make BP algorithm can skip the local minimum and find the global minimum easily. Modeling and prediction for the product quality of a certain propylene rectifying column with the new Elman network and algorithm, Simulation results show that the new network and the strategy can improve the network's training speed and the predictive precision of the product quality index effectively.Fifth International Conference on Natural Computation, ICNC 2009, Tianjian, China, 14-16 August 2009, 6 Volumes; 01/2009 - JIPS. 01/2009; 5:41-68.
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**ABSTRACT:**Detection of license plates is a critical part of License Plate Recognition (LPR) systems. In this paper we employ methods based on Probabilistic Principal Component Analysis (PPCA) to extract license plate areas out of vehicle images, and apply an im- proved normalization step to data preprocessing. The analysis of our experiment shows that this algorithm achieves good accuracy dealing with still vehicle images.ACM SIGSOFT Software Engineering Notes 05/2013; 38(3):1-4.

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