Application of the Chaos-RBF Neural Network on Oil Film Parameters Identification to Gasoline Engines under Transient Conditions

Author

Li Yuelin ; Ding Jingfeng ; Yang Wei ; Lu Dongxu

Author_Institution

Sch. of Automotive & Mech. Eng., Changsha Univ. of Sci. & Technol., Changsha, China

fYear

2013

fDate

6-7 Nov. 2013

Firstpage

147

Lastpage

152

Abstract

As it was difficult to determine the transient operating conditions of oil film parameter accurately, this article put forward an oil film parameter distinguish method in the gasoline engine transient conditions based on Chaos-RBF. The recognition ability with least square method was compared and analyzed, the Chaos-RBF neural network verified model has better ability of nonlinear identification which could effectively improve the oil film dynamic parameter identification precision and oil film dynamic characteristics under different working conditions was obtained.

Keywords

internal combustion engines; least squares approximations; mechanical engineering computing; parameter estimation; radial basis function networks; chaos-RBF neural network verified model; gasoline engines; least square method; nonlinear identification; oil film dynamic characteristics; oil film dynamic parameter identification precision; recognition ability; transient condition; Calibration; Chaos; Engines; Films; Fuels; Mathematical model; Neural networks; FPGA; Intel8080 interface; SDRAM; STM32; altlvds_tx;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems Design and Engineering Applications, 2013 Fourth International Conference on

Conference_Location

Zhangjiajie

Print_ISBN

978-1-4799-2791-3

Type

conf

DOI

10.1109/ISDEA.2013.438

Filename

6843415