基于三级神经网络的频谱识别

Xianjiang Meng, Xianli Meng
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引用次数: 1

摘要

本文提出了一种新的三级神经网络来识别生物表面的种类。利用自制的光纤传感器光谱仪对苹果表面有斑点的微区(380 ~ 780nm)进行可见光谱测量。为了对苹果进行分类,设计了一种单隐层BP-ANN自动识别生物表面特征。为了提高BP的性能,设计了一个三阶段BP- ann来识别四种苹果:无斑点的、撞坏的、害怕的和腐烂的。研究了不同输出范围下人工神经网络的性能,以及在输入信号中加入噪声对人工神经网络的影响。四类样本分别选取25、10、10、10个样本作为训练样本,分别选取10、10、10、10个样本作为测试样本。实验证明,如果加入10%的噪声,这种BP-ANN的准确率可以达到90%。
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Spectrum Recognition with Three-Stage Neural Network
In this paper, a new kind of three-stage neural network was developed to identify the sorts of the biological surface. The visible spectrum (from 380nm to 780nm) of the micro areas with some specks on the surface of the apples was measured with the self-made fiber sensor spectrometer. To sort the apples, A kind of BP-ANN with single hidden layer was devised to identify the characteristics on the biological surface automatically. To improve the performance of BP, A three-stage BP-ANN was devised to identify the four sorts of the apples, the fleckless, the bumped, the scared, and the rotten. It was also studied that the performance of the ANN with the different ranges of the output, the influence to the ANN if the noise was added to the input signals. 25,10,10 and 10 samples of four sorts were selected as training samples respectively, and 10,10,10 and 10 respectively were selected as testing samples. It proved that this kind of BP-ANN can achieve 90% accuracy if 10% noise was added.
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