Computational intelligence and low cost sensors in biomass combustion process

Jan Pital, Jozef Mizak
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引用次数: 18

Abstract

Artificial intelligence techniques have been used for carbon monoxide and oxygen low cost sensors signal processing in biomass combustion. Considering a large scatter of the measured data two approximation tools using artificial neural networks have been tested for approximation of carbon monoxide emissions dependence on oxygen concentration in the flue gas: AForge. Neuro library and Neural Network Fitting Tool of Matlab. The comparable results of approximation have been obtained by testing of both approximation tools on the off-line measured data.
生物质燃烧过程中的计算智能和低成本传感器
人工智能技术已被用于生物质燃烧中一氧化碳和氧气低成本传感器的信号处理。考虑到测量数据的大量分散,已经测试了两种使用人工神经网络的近似工具来近似一氧化碳排放量依赖于烟道气中的氧浓度:AForge。Matlab的神经库和神经网络拟合工具。通过对两种近似工具在离线测量数据上的测试,得到了近似结果的可比性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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