【作者】 高大启；

【Author】 Gao Daqi College of information, East China University of Science & Technology, Shanghai 200237

【机构】 华东理工大学信息学院；

【摘要】 本文建立了一种机器嗅觉装置,提出了一种组合化模块感知器。每一个模块由多个单输入—单输出的感知器所组成,每一个感知器被看成是一个专家。一个感知器模块决定一种特定气味及其强度,以及某一样本与该气味的相似程度。在多个模块中,气味的类别与强度由那个相似程度最高的模块所决定。通过将输入分量大小成比例变化到[0,6．0]的范围内,并将活化函数改为f(x)=3(1+exp(-x／3)-1这两种方法,感知器的学习速度显著加快。对乙醇、乙酸乙酯、乳酸乙酯和己酸乙酯等4种香料的实验结果表明,本文方法可有效地解决气味类别与浓度的同时估计问题。更多还原

【Abstract】 This paper sets up an electronic nose, and presents a kind of combinative and modular single-hidden-layer perceptrons. Every module is made up of multiple single-input and single-output multilayer percep-trons (MLPs). One MLP is regarded as an expert, and one module consists of several such experts. In electronic nose, one module is behalf of a kind of odors, and determines the similar degree of an odor, namely its strength. The most similar module gives the class and strength of the odor. By means of enlarging the input components to the range of [0, 6.0] and transforming the standard sigmoid activation functions to be f(x)= 3(1+ exp(-x/3)-1, the learning speeds of MLPs are sped up. The experiment for simultaneously estimating the classes and concentrations of 4 kinds of fragrant materials, namely ethanol, ethyl acetate, ethyl caproate and ethyl lactate in different concentrations, shows that the proposed method is quite effective.更多还原