A novel approach to fuel injection control using a radial basis function network

Author

Manzie, Chris ; Palaniswami, Marimuthu ; Watson, Harry

Author_Institution

Dept. of Electr. & Electron. Eng., Melbourne Univ., Parkville, Vic., Australia

Volume

2

fYear

1998

fDate

4-9 May 1998

Firstpage

986

Abstract

Proposes a radial basis function (RBF) based approach for the fuel injection control problem. In the past neural controllers for this problem have centred on using a CMAC type neural network with some success. Here we show that an RBF network with a fraction of the size of the CMAC network is capable of delivering superior control performance on a mean value engine model simulation. The proposed approach requires no a priori knowledge of the engine subsystems, and online learning is achieved using LMS updates

Keywords

feedforward neural nets; internal combustion engines; learning (artificial intelligence); least mean squares methods; neurocontrollers; LMS updates; fuel injection control; mean value engine model simulation; online learning; radial basis function network; Automatic control; Engine cylinders; Fuels; Manufacturing; Neural networks; Optimal control; Pollution measurement; Radial basis function networks; Size control; Weight control;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on

Conference_Location

Anchorage, AK

ISSN

1098-7576

Print_ISBN

0-7803-4859-1

Type

conf

DOI

10.1109/IJCNN.1998.685905

Filename

685905