首页|Reports Summarize Machine Learning Study Results from Faculty of Engineering and Architecture (Predicting compressor mass flow rate using various machine learni ng approaches)
Reports Summarize Machine Learning Study Results from Faculty of Engineering and Architecture (Predicting compressor mass flow rate using various machine learni ng approaches)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on artificial intell igence have been published. According to news originating from the Faculty of En gineering and Architecture by NewsRx correspondents, research stated, "A major f ocus of the present study is to construct high-fidelity models for predicting co rrected mass flow rates based on the collected compressor map data."The news editors obtained a quote from the research from Faculty of Engineering and Architecture: "Both traditional machine learning research and modern deep le arning techniques have been employed to obtain well-fitted regression models of compressor mass flow rate. As traditional machine learning methods, Multiple Lin ear Regression and Random Forest, are conducted on compressor maps for predictio n of corrected mass flow rate. The time series-based deep learning models are ab le to capture the overall trend of a given input for specific map data."
Faculty of Engineering and ArchitectureCyborgsEmerging TechnologiesMachine Learning
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