control-oriented modeling

In this paper, an improved, real-time, highly accurate control-oriented style, named Neuro Mean Value Modeling, is presented for IC engine modeling. This model is a combination of neural networks and mean value model, and is able to overcome the shortcomings of both styles. In other words, taking advantage of both methods, this -box extension will be of more reliability than a mere black-box neural network, and also of more accuracy than roughly white-box mathematical relations of In this paper, the model is modified to become suitable for designing an engine controller. Thanks to the sophisticated methods applied (such as committee method, improved partitioning, and especially, simplifying neural networks’ tasks), neural networks of high accuracy with line-like regressions will be achieved. As will be seen, the model is precisely validated - and it is capable of accurately predicting the engine’s outputs (such as pollutant emissions, manifold pressure, knock probability, and engine speed) all in real time. In the end, the effect of engine control inputs on pollutant emissions and fuel consumption will be examined. The engine employed to establish the model is a ported fuel injection SI engine.