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

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.