文献类型：期刊文献

英文题名：Performance evaluation of multilayer perceptrons for discriminating and quantifying multiple kinds of odors with an electronic nose

作者：Gao Daqi[1];Yang Zeping[1];Cai Chaoqian[1];Liu Fangjun[1]

机构：[1]E China Univ Sci & Technol, Dept Comp Sci, State Key Lab Bioreactor Engn, Shanghai 200237, Peoples R China

年份：2012

卷号：33

起止页码：204

外文期刊名：NEURAL NETWORKS

收录：;EI(收录号：20122915265371);WOS:【SCI-EXPANDED(收录号:WOS:000307430900019)】；

基金：This work is funded by the National Science Foundation of China (NSFC) under Grant Nos. 21176077 and 60675027, the High-Tech Development Program of China (863) under Grant No. 2006AA10Z315, and the Open Funding Project of the State Key Laboratory of Bioreactor Engineering.

语种：英文

外文关键词：Perceptrons; Local learning; Local generalization; Discrimination; Quantification; Odors

摘要：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) perceptions for quantification. (D) MSO perceptrons for discrimination followed by MSO perceptions 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 OM 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. (C) 2012 Elsevier Ltd. All rights reserved.