Pregled bibliografske jedinice broj: 151379
Neuro-Fuzzy Modelling of Marine Diesel Engine Cylinder Dynamics
Neuro-Fuzzy Modelling of Marine Diesel Engine Cylinder Dynamics // Control Applications in Marine Systems 2004: A Proceedings Volume from the IFAC Conference / Katebi, Reza ; Longhi, Sauro (ur.).
Amsterdam: Elsevier, 2004. str. 95-100 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 151379 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Neuro-Fuzzy Modelling of Marine Diesel Engine Cylinder Dynamics
Autori
Antonić, Radovan ; Vukić, Zoran ; Ognjen, Kuljača
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Control Applications in Marine Systems 2004: A Proceedings Volume from the IFAC Conference
/ Katebi, Reza ; Longhi, Sauro - Amsterdam : Elsevier, 2004, 95-100
ISBN
978-0080441696
Skup
IFAC Conference on Control Applications in Marine Systems
Mjesto i datum
Ancona, Italija, 07.07.2004. - 09.07.2004
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Marine diesel engine test bed; expert knowledge; data pre-processing; fuzzy model; fuzzy clustering; neuro-fuzzy inference
Sažetak
The practical application of some well recognized fuzzy methods and neural networks techniques to modelling marine diesel engine cylinder dynamics using real-time data and expert knowledge has been considered. The simulation was done in Matlab environment with real-time data originated from 2-stroke marine diesel propulsion engine on test bed during final testing, combined with knowledge elicited from engine experts and experienced test bed operators. Takagi-Sugeno fuzzy model has been designed based on cylinder pressure data after their clustering using fuzzy subtractive method. Model parameter tuning was inavestigated using ANFIS with combined learning algorithms: least squares and back propagation gradient descent method. The model obtained can be of practical importance in engine working regime adjustment, predicting cylinder data in faulty sensor case or adaptive threshold tuning within faults detection and identification.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika
Napomena
IPV - IFAC Proceedings Volume