级联
连接(主束)
人工智能
人工神经网络
模式识别(心理学)
噪音(视频)
分光计
计算机科学
光谱(功能分析)
集合(抽象数据类型)
灵敏度(控制系统)
生物系统
数学
光学
生物
物理
工程类
化学
电子工程
图像(数学)
几何学
色谱法
量子力学
程序设计语言
作者
Wei Shi,Yong Yao,Tieqiang Zhang,Xianjiang Meng
出处
期刊:PubMed
日期:2008-05-01
卷期号:28 (5): 983-7
摘要
A method of recognizing the visible spectrum of micro-areas on the biological surface with cascade-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.
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