L-M 학습 방법의 BP 신경망을 이용한 가스 센싱 시스템의 분류와 농도 추정

다국어 초록 (Multilingual Abstract)

In this paper, we have investigated the properties of multi-layer perceptron (MLP) for odour patterns classification and concentration estimation simultaneously. When the MLP may be has a fast convergence speed with small error and excellent mapping ability for classification, it can be possible to use for classification and concentration prediction of volatile chemicals simultaneously.
However, the conventional MLP, which is back-propagation of error based on the steepest descent method, was difficult to use for odour classification and concentration estimation simultaneously, because it is slow to converge and may fal1 into the local minimum. We adapted the Levenberg-Marquardt(LM) algorithm［4,5］having advantages both the steepest descent method and Gauss-Newton method instead of the conventional steepest descent method for the simultaneous classification and concentration estimation of odours. And, We designed the artificial odour sensing system(Electronic Nose) and applied LM-BP algorithm for classification and concentration prediction of VOC gases.

목차 (Table of Contents)

• 1.서론
• 2.센서 어레이와 후각인식시스템
• 3.LM-BP 신경회로망
• 4.LMBP 알고리즘을 이용한 분류 및 농도 추정
• 5.실험결과
• 6.결론