| 基于蚁群神经网络的目标图像识别 |
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| 引用本文： | 王永学,陈梧华,张建. 基于蚁群神经网络的目标图像识别[J]. 实验室科学, 2010, 13(1): 72-74. DOI: 10.3969/j.issn.1672-4305.2010.01.027 |
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| 作者姓名： | 王永学 陈梧华 张建 |
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| 作者单位： | 河北工业大学理学院,天津,300401 |
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| 摘 要： | 该文提出一种基于蚁群优化与人工神经网络相结合的识别算法。该方法能够防止BP网络陷入局部极小点,且收敛速度快。针对飞机图像目标识别,提取图像三阶相关量特征、不变矩特征和图像边界的Fourier描述子特征,形成特征向量作为神经网络的输入向量。仿真实验表明,新算法能够有效缩短网络训练时间,提高目标识别精度。
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| 关 键 词： | 目标识别 特征提取 神经网络 蚁群优化 |
Image recognition based on ant colony optimization neural network |
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| WANG Yong-xue,CHEN Wu-hua,ZHANG Jian. Image recognition based on ant colony optimization neural network[J]. Laboratory Science, 2010, 13(1): 72-74. DOI: 10.3969/j.issn.1672-4305.2010.01.027 |
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| Authors: | WANG Yong-xue CHEN Wu-hua ZHANG Jian |
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| Affiliation: | (School of Science, Hebei University of Technology, Tianjin 300401 ; China) |
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| Abstract: | In this paper, a recognition algorithm based on ant colony optimization and neural network is proposed. It overcomes the shortcomings of traditional BP algorithmn and converges fast. According to the characteristics of plane target images, the three local features of the line moments, features of sub-block and the contour curve＇ s shape are adopted. The results of experiments prove that the presented algorithm can shorten the training time effectively and increase the accuracy of recognition. |
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| Keywords: | target recognition feature extraction neural network ant colonY optimization |
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