RESEARCH INITIATION AWARD: The Application of Neural Networks to Gas Sensors

研究启动奖：神经网络在气体传感器中的应用

• 批准号: 9309012
• 负责人: Bruce Segee
• 金额: $ 7万
• 依托单位: University of Maine
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-09-01 至 1998-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9309012&HistoricalAwards=false
• 关键词: RESEARCH; INITIATION; AWARD; Application; Neural

9309012 Segee Sensor technology exist that is capable of detecting potentially dangerous gases even in very low concentrations. However, these sensors are not very selective and often have responses that are nonlinear function of gas concentration. Selectivity has been improved by using an array of sensors having differing sensitivities. The mathematics of calibrating such an array is very complex and poor performance and even instability can result if the responses of individual sensors are too similar. If its generally necessary to disregard valid sensor data from a subset of the available sensor. To address the above problem artificial neural network models have been suggested as an alternative. They are capable of learning multidimensional nonlinear functions, can exploit redundant information and are adaptable. The proposed work will use data gathered form sensors in a controlled environment to train three different types of artificial neural networks to identify gas type and concentration. Performance of these neural networks will be measured first by means of a test set gathered at the same time as the training data, but not used for training. Ultimately, performance in situ will be measured and on-line training used to fine tune performance. ***

9309012 Segee传感器技术的存在，能够检测潜在的危险气体，即使在非常低的浓度。然而，这些传感器不是很有选择性，并且通常具有气体浓度的非线性函数响应。通过使用具有不同灵敏度的传感器阵列，选择性得到了提高。校准这种阵列的数学计算非常复杂，如果单个传感器的响应过于相似，则可能导致性能差甚至不稳定。如果通常需要忽略来自可用传感器子集的有效传感器数据。为了解决上述问题，人工神经网络模型被建议作为一种替代方法。它们具有学习多维非线性函数、挖掘冗余信息和适应性强的特点。这项工作将使用在受控环境中从传感器收集的数据来训练三种不同类型的人工神经网络，以识别气体类型和浓度。这些神经网络的性能将首先通过与训练数据同时收集的测试集来测量，但不用于训练。最终，在现场的表现将被测量和在线培训用于微调性能。* * *