生物柴油质量统计和神经网络工具的最新进展

Q4 Computer Science
Delano Brandes Marques, A. G. O. Filho, A. Romariz, I. Viegas, Djavania A. Luz, A. K. D. B. Filho, S. Labidi, A. Ferraudo
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引用次数: 7

摘要

比较了传统的线性回归和人工神经网络(ANN)技术在预测生物柴油及其混合物性能方面的有效性。本文综述了统计和人工神经网络在生物柴油质量分析中的应用。本文还提出了一个案例研究,首次使用其他官方质量参数而不是化学成分作为输入数据来预测生物柴油的氧化稳定性。从这个意义上说,我们希望这篇论文能够补充最近的一系列综述论文,并促进这一快速发展领域的未来研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Recent Developments on Statistical and Neural Network Tools Focusing on Biodiesel Quality
The performance of both the traditional linear regression and Artificial Neural Network (ANN) techniques has been compared to check the validity to predict the properties of biodiesel and mixtures of diesel and biodiesel. We present on this paper a review on statistical and ANN applications to the Biodiesel quality. A case study is also presented showing the prediction of oxidative stability of Biodiesel using, for the first time, other official quality parameters instead of the chemical composition as input data. In this sense, our hope is that this paper would complement a series of recent review papers and catalyze future research in this rapidly evolving area.
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来源期刊
International Journal of Computer Science and Applications
International Journal of Computer Science and Applications Computer Science-Computer Science Applications
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期刊介绍: IJCSA is an international forum for scientists and engineers involved in computer science and its applications to publish high quality and refereed papers. Papers reporting original research and innovative applications from all parts of the world are welcome. Papers for publication in the IJCSA are selected through rigorous peer review to ensure originality, timeliness, relevance, and readability.
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