A hinge neural network approach for the identification and optimization of diesel engine emissions

Characteristics such as nonlinearity, uncertainty, and time-delays make the identification of dynamic processes challenging. One means of addressing this problem is to develop approaches based on artificial neural networks that are capable of modeling highly nonlinear systems. The aim of this paper is to present the nonlinear mathematical models of exhaust gas formations achieved with a certain Neural Network (NN) architecture, namely Hinging Hyperplane Trees (HHT). A brief description of HHT is followed by a presentation of identification of nitrogen-oxides and opacity emissions for a Diesel engine. Experimental results that show the effectiveness of the HHT approach are included. The article concludes with the description of an optimization environment for Diesel engine management based upon previously achieved emission models, and which is used in order to obtain an optimal performance regarding fuel consumption, emissions and drivability. Copyright (C) 2001 IFAC.