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

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