Estimation of Gas Turbine Shaft Torque and Fuel Flow of a CODLAG Propulsion System Using Genetic Programming Algorithm

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Misc{DBLP:journals/corr/abs-2012-03527,

• author = "Nikola Andelic and Sandi {Baressi Segota} and Ivan Lorencin and Zlatan Car",
• title = "Estimation of Gas Turbine Shaft Torque and Fuel Flow of a {CODLAG} Propulsion System Using Genetic Programming Algorithm",
• howpublished = "arXiv",
• volume = "abs/2012.03527",
• year = "2020",
• month = "7 " # dec,
• keywords = "genetic algorithms, genetic programming, Artificial Intelligence, Combined Diesel-Electric and Gas Propulsion System, Genetic Programming Algorithm, Gas Turbine Shaft Torque Estimation, Fuel Flow Estimation",
• eprinttype = "arXiv",
• eprint = "2012.03527",
• timestamp = "Wed, 09 Dec 2020 00:00:00 +0100",
• biburl = "https://dblp.org/rec/journals/corr/abs-2012-03527.bib",
• bibsource = "dblp computer science bibliography, https://dblp.org",
• URL = "https://arxiv.org/abs/2012.03527",
• size = "25 pages",
• abstract = "he publicly available dataset of condition based maintenance of combined diesel-electric and gas (CODLAG) propulsion system for ships has been used to obtain symbolic expressions which could estimate gas turbine shaft torque and fuel flow using genetic programming (GP) algorithm. The entire dataset consists of 11934 samples that was divided into training and testing portions of dataset in an 80:20 ratio. The training dataset used to train the GP algorithm to obtain symbolic expressions for gas turbine shaft torque and fuel flow estimation consisted of 9548 samples. The best symbolic expressions obtained for gas turbine shaft torque and fuel flow estimation were obtained based on their R2 score generated as a result of the application of the testing portion of the dataset on the aforementioned symbolic expressions. The testing portion of the dataset consisted of 2386 samples. The three best symbolic expressions obtained for gas turbine shaft torque estimation generated R2 scores of 0.999201, 0.999296, and 0.999374, respectively. The three best symbolic expressions obtained for fuel flow estimation generated R2 scores of 0.995495, 0.996465, and 0.996487, respectively.",
• notes = "Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia",

}

Genetic Programming entries for Nikola Andelic Sandi Baressi Segota Ivan Lorencin Zlatan Car

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