基于级联人工神经网络的生物表面光谱识别方法

A Method of Recognizing Biology Surface Spectrum Using Cascade-Connection Artificial Neural Nets

提出了一种利用多级级联人工神经网络对生物表面微区的可见光光谱进行识别与分类的方法。该方法利用自组装光纤探头式光谱仪对苹果表面微区500-730nm范围内的可见光光谱进行测量，光谱间隔5nm，记录光谱测量数据并依据光谱测量数据建立由三个单隐层、四十七个输入、单输出的人工神经网络级联而成的光谱识别系统。实验表明该级联系统可以对苹果的烂痕、疤痕、碰痕的反射光谱进行准确识别，在5％和15％的噪声影响下其识别准确率分别能达到97％和85％以上，克服了单级人工神经网络识别准确率不高、抗噪声能力差等缺点。最后文章提出了一种识别结果的隶属度表示法，该方法借鉴模糊数学中隶属度的概念，可以实现对识别结果客观、准确的表征。

A method of recognizing the visible spectrum of micro-areas on the biological surface with caseade-connection artificial neural nets is presented in the present paper. The visible spectra of spots on apples＇ pericarp, ranging from 500 to 730 nm, were obtained with a fiber-probe spectrometer, and a new spectrum recognition system consisting of three-level cascade-connection neural nets was set up. The experiments show that the spectra of rotten, scar and bumped spot on an apple＇ s pericarp can be recognized by the spectrum recognition system, and the recognition accuracy is higher than 85% even when noise level is 15%. The new recognition system overcomes the disadvantages of poor accuracy and poor anti-noise with the traditional system based on single cascade neural nets. Finally, a new method of expression of recognition results was proved. The method is based on the conception of degree of membership in fuzzing mathematics, and through it the recognition results can be expressed exactly and objectively.