Use of Genetic Programming for the Estimation of CODLAG Propulsion System Parameters

Anđelić, Nikola; Baressi Šegota, Sandi; Lorencin, Ivan; Poljak, Igor; Mrzljak, Vedran; Car, Zlatan

doi:10.3390/jmse9060612

Pregled bibliografske jedinice broj: 1130381

Use of Genetic Programming for the Estimation of CODLAG Propulsion System Parameters

Anđelić, Nikola; Baressi Šegota, Sandi; Lorencin, Ivan; Poljak, Igor; Mrzljak, Vedran; Car, Zlatan

Use of Genetic Programming for the Estimation of CODLAG Propulsion System Parameters // Journal of marine science and engineering, 9 (2021), 6; 612, 31 doi:10.3390/jmse9060612 (međunarodna recenzija, članak, znanstveni)

CROSBI ID: 1130381 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Use of Genetic Programming for the Estimation of CODLAG Propulsion System Parameters

Autori
Anđelić, Nikola ; Baressi Šegota, Sandi ; Lorencin, Ivan ; Poljak, Igor ; Mrzljak, Vedran ; Car, Zlatan

Izvornik
Journal of marine science and engineering (2077-1312) 9 (2021), 6; 612, 31

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
CODLAG ; Data-driven modelling ; Genetic programming ; Decay state coefﬁcients

Sažetak
In this paper, the publicly available dataset for the Combined Diesel-Electric and Gas (CODLAG) propulsion system was used to obtain symbolic expressions for estimation of fuel ﬂow, ship speed, starboard propeller torque, port propeller torque, and total propeller torque using genetic programming (GP) algorithm. The dataset consists of 11, 934 samples that were divided into training and testing portions in an 80:20 ratio. The training portion of the dataset which consisted of 9548 samples was used to train the GP algorithm to obtain symbolic expressions for estimation of fuel ﬂow, ship speed, starboard propeller, port propeller, and total propeller torque, respectively. After the symbolic expressions were obtained the testing portion of the dataset which consisted of 2386 samples was used to measure estimation performance in terms of coefﬁcient of correlation (R2) and Mean Absolute Error (MAE) metric, respectively. Based on the estimation performance in each case three best symbolic expressions were selected with and without decay state coefﬁcients. From the conducted investigation, the highest R2 and lowest MAE values were achieved with symbolic expressions for the estimation of fuel ﬂow, ship speed, starboard propeller torque, port propeller torque, and total propeller torque without decay state coefﬁcients while symbolic expressions with decay state coefﬁcients have slightly lower estimation performance.

Izvorni jezik
Engleski

Znanstvena područja
Brodogradnja, Računarstvo, Strojarstvo, Tehnologija prometa i transport

POVEZANOST RADA

Projekti:
IP-2018-01-3739 - Sustav potpore odlučivanju za zeleniju i sigurniju plovidbu brodova (DESSERT) (Prpić-Oršić, Jasna, HRZZ - 2018-01) ( CroRIS)
NadSve-Sveučilište u Rijeci-UNIRI_TEHNIC‐18‐18‐1146 - Nesigurnosti procjene brzine broda u pri realnim vremenskim uvjetima (Prpić-Oršić, Jasna, NadSve ) ( CroRIS)
NadSve-Sveučilište u Rijeci-uniri-tehnic-18-14 - Optimizacija dizalica topline i rashladnih sustava koji koriste radne tvari niskog utjecaja na globalno zatopljenje korištenjem numeričkih simulacija (Pavković, Branimir, NadSve - NATJEČAJ „UNIRI PROJEKTI“ Natječaj za dodjelu sredstava potpore znanstvenim istraživanjima na Sveučilištu u Rijeci za 2018. godinu - projekti iskusnih znanstvenika i umjetnika od 03. 09. 2018.) ( CroRIS)
NadSve-Sveučilište u Rijeci-uniri-tehnic-18-275-1447 - Razvoj inteligentnog ekspertnog sustava za online diagnostiku raka mokračnog mjehura (Car, Zlatan, NadSve - UNIRI potpore) ( CroRIS)
--KK.01.2.2.03.0004 - Centar kompetencija za pametne gradove (CEKOM) (Car, Zlatan; Slavić, Nataša; Vilke, Siniša) ( CroRIS)
InoUstZnVO-CIII-HR-0108-10 - Concurrent Product and Technology Development - Teaching, Research and Implementation of Joint Programs Oriented in Production and Industrial Engineering (Car, Zlatan, InoUstZnVO - CEEPUS) ( CroRIS)
Ostalo-CEI - 305.6019-20 - Use of regressive artificial intelligence (AI) and machine learning (ML) methods in modelling of COVID-19 spread (COVIDAi) (Car, Zlatan, Ostalo - CEI Extraordinary Call for Proposals 2020) ( CroRIS)
--KK.01.1.1.01.009 - Napredne metode i tehnologije u znanosti o podatcima i kooperativnim sustavima (DATACROSS) (Šmuc, Tomislav; Lončarić, Sven; Petrović, Ivan; Jokić, Andrej; Palunko, Ivana) ( CroRIS)