Add to ORKGThis paper presents some efforts on using neural networks to identify nonlinear dynamic models of the manifold pressure and the mass flow processes in automotive engines. External recurrent neural networks are used for dynamic mapping. The dynamic Levenberg-Marquardt algorithm is applied to the weight-estimation. Early results indicate that the neural network based modeling of the manifold dynamics can result in a model comparable if not better than the first principles based models
Advanced engine control systems require accurate models of the thermodynamic-mechanical process, whi...
An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way...
The paper deals with the simulation of the wall wetting dynamics in SI engines, making use of Recurr...
This paper presents a procedure for using neural networks to identify the nonlinear dynamic model of...
International audienceNeural networks are applied to the identification of non-linear structural dyn...
The nonlinear modelling ability of neural networks has been widely recognised as an effective tool t...
For modelling a dynamic system in practice, it often faces the difficulty in improving the accuracy ...
Abstract:- In this paper, nonlinear dynamical black-box models of a common rail injection system for...
The correct measurement of the intake air mass flow is fundamental for the Spark Ignition (SI) Engin...
In this work neural network models are used to reconstruct in-cylinder pressure from a vibration sig...
Developing and improving upon a good empirical model for an engine can be time-consuming and costly....
In order to maintain the air fuel ratio within the stoichiometric operating window, which is necessa...
The paper deals with the identification of recurrent neural networks (RNNs) for simulating the air–f...
The spark-ignition (SI) engine dynamics is described as a severely nonlinear and fast process. A bla...
A method for the development of mathematical models for dynamic systems with arbitrary nonlinearitie...
Advanced engine control systems require accurate models of the thermodynamic-mechanical process, whi...
An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way...
The paper deals with the simulation of the wall wetting dynamics in SI engines, making use of Recurr...
This paper presents a procedure for using neural networks to identify the nonlinear dynamic model of...
International audienceNeural networks are applied to the identification of non-linear structural dyn...
The nonlinear modelling ability of neural networks has been widely recognised as an effective tool t...
For modelling a dynamic system in practice, it often faces the difficulty in improving the accuracy ...
Abstract:- In this paper, nonlinear dynamical black-box models of a common rail injection system for...
The correct measurement of the intake air mass flow is fundamental for the Spark Ignition (SI) Engin...
In this work neural network models are used to reconstruct in-cylinder pressure from a vibration sig...
Developing and improving upon a good empirical model for an engine can be time-consuming and costly....
In order to maintain the air fuel ratio within the stoichiometric operating window, which is necessa...
The paper deals with the identification of recurrent neural networks (RNNs) for simulating the air–f...
The spark-ignition (SI) engine dynamics is described as a severely nonlinear and fast process. A bla...
A method for the development of mathematical models for dynamic systems with arbitrary nonlinearitie...
Advanced engine control systems require accurate models of the thermodynamic-mechanical process, whi...
An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way...
The paper deals with the simulation of the wall wetting dynamics in SI engines, making use of Recurr...