In this paper, nonlinear dynamical black-box models of a common rail injection system for a CNG engine are developed. In particular, the common rail pressure dynamics is modeled on the basis of three input signals, easily and cheaply measurable on board a vehicle. The nonlinear model is identified by means of Multi Layer Perceptron neural networks. Both non-autoregressive (NMAX) and autoregressive (NARMAX) models have been developed, showing satisfactory performance.

Neural Network Nonlinear Modeling of a Common Rail Injection System for a CNG Engine

Bruno Maione;Paolo Lino;Alessandro Rizzo
2004-01-01

Abstract

In this paper, nonlinear dynamical black-box models of a common rail injection system for a CNG engine are developed. In particular, the common rail pressure dynamics is modeled on the basis of three input signals, easily and cheaply measurable on board a vehicle. The nonlinear model is identified by means of Multi Layer Perceptron neural networks. Both non-autoregressive (NMAX) and autoregressive (NARMAX) models have been developed, showing satisfactory performance.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/9165
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