Drift counteraction with multiple self-organising maps for an electronic nose
Drift counteraction with multiple self-organising maps for an electronic nose
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DOI：
10.1016/j.snb.2003.10.029
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发表时间：
2004-03
影响因子：
8.4
通讯作者：
M. Zuppa;C. Distante;P. Siciliano;K. Persaud
中科院分区：
文献类型：
--
作者：
M. Zuppa;C. Distante;P. Siciliano;K. Persaud
在本文中，一个新的mSom神经网络方法已被开发和应用，以提高分类的气味类感测的多传感器系统作为电子鼻进行漂移。的mSom网络被证明是一个合适的技术来识别的化学传感器阵列的响应模式，其手段抵消的参数漂移问题。这种神经结构涉及使用多个自组织映射。每个映射近似于单个气味集的统计分布，并且它能够通过基于其经验的重复自训练过程来适应由于漂移效应而引起的输入概率分布的变化。这里提出的新mSom算法允许自主进行所需的再训练过程，一旦输入概率分布发生变化。为此，在网络测试阶段（网络性能）期间，还使用平滑滤波器来执行依赖于输入数据向量与映射码本向量之间的欧几里得距离的函数的分析。
In this paper, a new mSom neural network methodology has been developed and applied to improve the classification of odour classes sensed by a multisensor system as an electronic nose subjected to drift. The mSom network proved to be a suitable technique to recognise the response patterns of a chemical sensor array for its means of counteracting the parameter drift problem. This neural architecture involves the use of multiple self-organising maps. Each map approximates the statistical distribution of a single odour set and it is able to adapt itself to changes of input probability distribution due to drift effects by means of repetitive self-training processes based on its experience. The new mSom algorithm proposed here allows to carry out autonomously the needed retraining processes once the input probability distribution changes. At this aim, the analysis of the function dependent on the Euclidean distance between the input data vectors and map codebook vectors is performed also with the use of smoothing filters during the network testing phase (network performance).