Comparison of genetic algorithm and neural network approaches for the prognosis of mechanical idle running losses in agriculture tractor transmission

• Autorzy: Ivanov Rosen , Ivanova Donka

Identyfikatory

DOI

10.20858/tp.2022.17.3.05

• Języki publikacji: EN

Abstrakty

EN

An experimental investigation of mechanical idle running losses in an agriculture tractor transmission was used to collect a wide range of data. The influence of the engine rotation speed, the number of switched-on gears, and the oil level in the transmission gearbox on the idle running losses was determined. Adequate regression models in cases of switched-on and switched-off PTO were received. A genetic algorithm was used to optimize mathematical models obtained using regression analysis. A feedforward artificial neural network was also developed to estimate the same experimental data for mechanical idle running losses in transmission. A back-propagation algorithm was used when training and testing the network. A comparison of the correlation coefficient, reduced chi-square, mean bias error, and root mean square error between the experimental data and fit values of the obtained models was made. It was concluded that the neural network represented the mechanical idle running losses in tractor transmission more accurately than other models.

Słowa kluczowe

EN

tractor transmission efficiency; idle running; losses; neural network; genetic algorithm

PL

sprawność przekładni ciągnika; bieg jałowy; straty; sieć neuronowa; algorytm genetyczny

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Transport Problems
• Rocznik: 2022
• Tom: T. 17, z. 3
• Strony: 51--59
• Opis fizyczny: Bibliogr. 18 poz.

Twórcy

autor

Ivanov Rosen

• University of Ruse; Studentska 8, 7017 Ruse, Bulgaria

• https://orcid.org/0000-0002-0573-4316

autor

Ivanova Donka

• University of Ruse; Studentska 8, 7017 Ruse, Bulgaria

• https://orcid.org/0000-0002-3548-4610

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).