Modelling a variable valve timing spark ignition engine using different neural networks
Modelling a variable valve timing spark ignition engine using different neural networks
In this paper, different neural networks (NN) are compared for modelling a variable valve timing spark-ignition (VVT SI) engine. The overall system is divided for each output into five neural multi-input single-output (MISO) subsystems. Three kinds of NN, multilayer Perceptron (MLP), pseudo-linear radial basis function (PLRBF), and local linear model tree (LOLIMOT) networks, are used to model each subsystem. Real data were collected when the engine was under different operating conditions and these data are used in training and validation of the developed neural models. The obtained models are finally tested in a real-time online model configuration on the test bench. The neural models run independently of the engine in parallel mode. The model outputs are compared with process output and compared among different models. These models performed well and can be used in the model-based engine control and optimization, and for hardware in the loop systems.
Document information
Modelling a variable valve timing spark ignition engine using different neural networks
Modellierung eines Vergasermotors mit variabler Ventilsteuerung mit Hilfe unterschiedlicher neuronaler Netzwerke
2004
13 Seiten, 26 Quellen
Article (Journal)
English
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