A Method of Recognizing Biology Surface Spectrum Using Cascade-Connection Artificial Neural Nets
SHI Wei-jie1,YAO Yong1,ZHANG Tie-qiang2,MENG Xian-jiang3
1. Laser Information Technology Research Center, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China 2. Physics College, Nanling Campus, Jilin University, Changchun 130025, China 3. Communication College, Nanhu Campus, Jilin University, Changchun 130025, China
Abstract：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.
Key words：Cascade-connection neural nets;Biology spectrum;Degree of membership;Pattern recognition
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