Localized Model to Segmentally Estimate Miles per Gallon (MPG) for Equipment Engines

Abstract:

In this paper, we built a localized regression model to estimate the miles per gallon (MPG) characteristic for equipment engines based on a serious physical features of this engine. First, we statistically viewed these parameters to build up a basic understanding of the data we collected. Then, with the belief that engines with similar characteristics will perform similarly, we proposed a novel localized model with a novel optimal function based EM algorithm and a novel self-adjusted optimal clustering algorithm to estimate MPG based on the other fully studied engines with similar physical features.

• Periodical: Applied Mechanics and Materials (Volumes 556-562)
• Pages: 1069-1074
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.556-562.1069
• Online since: May 2014
• Authors: Jiu Lin Luo, Hao Jing Luo*, Ai Min Li, Hao Han Wang
• Keywords: Clustering, Engine Parameters, Fuel Economy, Machine Learning, Regression

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