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

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3 Author(s)
Manzie, C. ; Dept. of Electr. & Electron. Eng., Melbourne Univ., Parkville, Vic., Australia ; Palaniswami, M. ; Watson, H.

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

Published in:

Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on  (Volume:2 )

Date of Conference:

4-9 May 1998

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