« Previous AbstractRelationship between the Grade and the Characteristic Flavor of PCT (Panyong Congou Black Tea)    Next AbstractTheory and laboratory study of a tall passive chamber for measuring gas fluxes at soil surface »

Neural Netw

Title:		Performance evaluation of multilayer perceptrons for discriminating and quantifying multiple kinds of odors with an electronic nose
Author(s):		Gao D; Yang Z; Cai C; Liu F;
Address:		"Department of Computer Science, State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China. gaodaqi@ecust.edu.cn"
Journal Title:		Neural Netw
Year:		2012
Volume:		20120605
Issue:
Page Number:		204 - 215
DOI:		10.1016/j.neunet.2012.05.009
ISSN/ISBN:		1879-2782 (Electronic) 0893-6080 (Linking)
Abstract:		"This paper studies several types and arrangements of perceptron modules to discriminate and quantify multiple odors with an electronic nose. We evaluate the following types of multilayer perceptron. (A) A single multi-output (SMO) perceptron both for discrimination and for quantification. (B) An SMO perceptron for discrimination followed by multiple multi-output (MMO) perceptrons for quantification. (C) An SMO perceptron for discrimination followed by multiple single-output (MSO) perceptrons for quantification. (D) MSO perceptrons for discrimination followed by MSO perceptrons for quantification, called the MSO-MSO perceptron model, under the following conditions: (D1) using a simple one-against-all (OAA) decomposition method; (D2) adopting a simple OAA decomposition method and virtual balance step; and (D3) employing a local OAA decomposition method, virtual balance step and local generalization strategy all together. The experimental results for 12 kinds of volatile organic compounds at 85 concentration levels in the training set and 155 concentration levels in the test set show that the MSO-MSO perceptron model with the D3 learning procedure is the most effective of those tested for discrimination and quantification of many kinds of odors"
Keywords:		"*Discrimination Learning/physiology Electronics/*instrumentation/methods *Neural Networks, Computer *Nose/physiology *Odorants *Smell/physiology;"
Notes:		"MedlineGao, Daqi Yang, Zeping Cai, Chaoqian Liu, Fangjun eng Evaluation Study Research Support, Non-U.S. Gov't 2012/06/22 Neural Netw. 2012 Sep; 33:204-15. doi: 10.1016/j.neunet.2012.05.009. Epub 2012 Jun 5"