基于模糊ART神经网络与D-S证据理论相结合的汽车架梁品种在线识别

IF 0.6 Q4 AUTOMATION & CONTROL SYSTEMS

International Journal of Modelling Identification and Control Pub Date : 2009-11-18 DOI:10.1504/IJMIC.2009.029264

Hua Wang, Jin-gang Gao, Shuang Zhang

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引用次数: 2

摘要

针对数百条汽车架梁的人工识别困难，提出了一种综合机器视觉、小波变换理论、模糊ART神经网络和D-S证据理论的汽车架梁在线自动检测方法。首先，将局部熵、NMI和小波系数的能量值作为模糊ART神经网络的输入层，得到这三个不同特征的基本置信度;接下来，运用D-S证据理论对三个基本置信度进行融合。最后，获得汽车架梁图像的全置信度，以确定被检汽车架梁的模型。本课题利用D-S证据理论和模糊ART神经网络对汽车架梁进行品种识别，解决了单一字符模板识别率较低的问题，提出了一种多字符融合的方法，为国内外应用提供了一种新技术。

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Online variety recognition of auto rack girders based on combination of Fuzzy ART neural network with D-S evidence theory

To address the difficulty of artificial recognition of hundreds of auto rack girders, this paper introduces an online automatic inspection method which synthesises machine vision, wavelet transformation theory, Fuzzy ART neural networks and D-S evidence theory on auto rack girders. First, local entropy, NMI and energy value of wavelet coefficients are used as input layers of a Fuzzy ART neural network, to gain the basic confidences of these three different characters. Next, D-S evidence theory is used to fuse the three basic confidences. Finally, total confidence in auto rack girder images, is obtained to determine a model for the inspected auto rack girders. This project of variety recognition for auto rack girders using D-S evidence theory and the Fuzzy ART neural network provides a new technology for use at home or overseas, which resolves the question of the lower recognition rate for a single character template and advances a method for multi-character fusion.

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来源期刊

International Journal of Modelling Identification and Control AUTOMATION & CONTROL SYSTEMS-

CiteScore

1.70

自引率

57.10%

发文量

期刊介绍： Most of the research and experiments in the fields of science, engineering, and social studies have spent significant efforts to find rules from various complicated phenomena by observations, recorded data, logic derivations, and so on. The rules are normally summarised as concise and quantitative expressions or “models". “Identification" provides mechanisms to establish the models and “control" provides mechanisms to improve the system (represented by its model) performance. IJMIC is set up to reflect the relevant generic studies in this area.