利用反射光谱和人工神经网络技术研究生物表面

Xianjiang Meng, Tie-qiang Zhang
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引用次数: 0

摘要

提出了一种利用反向传播人工神经网络(BP-ANN)识别生物表面微区可见光谱的方法。用自制的光纤传感器光谱仪测量了苹果表面微斑区500nm ~ 730nm的可见光谱。设计了一种单隐层bp神经网络,用于生物表面特征的自动识别。研究了不同输出范围、不同单隐层单元数、输入信号中加入噪声对人工神经网络性能的影响。最后建立三阶段人工神经网络,分别选取25、10、10、10个苹果作为训练样本,选取15、10、10、10个苹果作为测试样本。如果加入20%的噪声,BP-ANN的准确率可以达到85%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Study on the Biological Surface Using the Reflected Spectrum and Artificial Neural Network Technology
This paper gave a method to identify the visible spectrum of micro areas on the biological surface with the back propagation artificial neural network (BP-ANN).The visible spectrum (from 500nm to 730nm) of the micro areas with some specks on the surface of the apples was measured with the self- made fiber sensor spectrometer. A kind of BP-ANN with single hidden layer was devised to identify the characteristics on the biological surface automatically. It was also studied that the performance of the ANN with the different ranges of the output, the different numbers of the single hidden layer's units, the influence to the ANN if the noise was added to the input signals. Finally a three-stage ANN was founded to identify the four sorts of apples, the fleckless, the pushed, the scared, and the rotten�» 25,10,10 and 10 respectively were selected as training samples, and 15,10,10 and 10 respectively were selected as testing samples. This BP-ANN can achieve 85% accuracy if 20% noise was added.
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