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Entropy 2015, 17(6), 3857-3876; doi:10.3390/e17063857

Radial Wavelet Neural Network with a Novel Self-Creating Disk-Cell-Splitting Algorithm for License Plate Character Recognition

1
School of Science, North University of China, Shanxi, Taiyuan 030051, China
2
School of Information and Communication Engineering, North University of China, Shanxi, Taiyuan 030051, China
*
Author to whom correspondence should be addressed.
Academic Editor: J. A. Tenreiro Machado
Received: 20 April 2015 / Revised: 25 May 2015 / Accepted: 3 June 2015 / Published: 9 June 2015
(This article belongs to the Section Complexity)
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Abstract

In this paper, a novel self-creating disk-cell-splitting (SCDCS) algorithm is proposed for training the radial wavelet neural network (RWNN) model. Combining with the least square (LS) method which determines the linear weight coefficients, SCDCS can create neurons adaptively on a disk according to the distribution of input data and learning goals. As a result, a disk map is made for input data as well as a RWNN model with proper architecture and parameters can be decided for the recognition task. The proposed SCDCS-LS based RWNN model is employed for the recognition of license plate characters. Compared to the classical radial-basis-function (RBF) network with K-means clustering and LS, the proposed model can make a better recognition performance even with fewer neurons. View Full-Text
Keywords: radial wavelet neural network; self-creating disk-cell-splitting; character recognition radial wavelet neural network; self-creating disk-cell-splitting; character recognition
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Cheng, R.; Bai, Y.; Hu, H.; Tan, X. Radial Wavelet Neural Network with a Novel Self-Creating Disk-Cell-Splitting Algorithm for License Plate Character Recognition. Entropy 2015, 17, 3857-3876.

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