Journal ArticleDOI
Application of neural network in the study of combustion rate of natural gas/diesel dual fuel engine
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TLDR
It was proved that the developed combustion rate model could be used to successfully predict and optimize the combustion process of dual fuel engine.Abstract:
In order to predict and improve the performance of natural gas/diesel dual fuel engine (DFE), a combustion rate model based on forward neural network was built to study the combustion process of the DFE. The effect of the operating parameters on combustion rate was also studied by means of this model. The study showed that the predicted results were good agreement with the experimental data. It was proved that the developed combustion rate model could be used to successfully predict and optimize the combustion process of dual fuel engine.read more
Citations
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Journal ArticleDOI
Prediction of automotive engine power and torque using least squares support vector machines and Bayesian inference
TL;DR: The study shows that the predicted results using the estimated model from LS-SVM are good agreement with the actual test results, and Bayesian framework is also applied to infer the hyperparameters used in LS- SVM so as to eliminate the work of cross-validation.
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Adaptive PID Controller based on Reinforcement Learning for Wind Turbine Control
TL;DR: A self tuning PID control strategy using reinforcement learning is proposed in this paper to deal with the control of wind energy conversion systems (WECS) and it is perfectly adaptable and strongly robust, which is better than that of a conventional PID controller.
Journal ArticleDOI
Study of CNG/diesel dual fuel engine's emissions by means of RBF neural network
Zhen-tao Liu,Shao-mei Fei +1 more
TL;DR: In this article, an emission prediction model for CNG/diesel DFE based on RBF neural network was built for analyzing the effect of the main performance parameters on the CO, NOx emissions of DFE.
Journal ArticleDOI
Data preprocessing and modelling of electronically-controlled automotive engine power performance using kernel principal components analysis and least squares support vector machines
TL;DR: Experimental results show that KPCA+LS-SVM can really improve the training time and accuracy of an engine model.
Journal ArticleDOI
Identification of key design parameters of high-speed train for optimal design
TL;DR: A new method to identify the key design variables against the running performance indicators based on the sensitivity analysis is presented, which in turn bases itself on simulation-oriented surrogate models and can reduce the simulation time greatly and the design variable space with the key variables will be reduced significantly.
References
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Book
Neuro-Control and its Applications
Marzuki Khalid,Rubiyah Yusof +1 more
TL;DR: This chapter discusses the application of Neuro-Control to a Water-Bath Process and Comparison with Alternative Control Schemes, and some Discussions on On-Line Learning.
Proceedings ArticleDOI
Multi-Dimensional Modeling of Natural Gas Ignition Under Compression Ignition Conditions Using Detailed Chemistry
Book ChapterDOI
Neuro-Control Applications
TL;DR: In this chapter, several neuro-control techniques with applications to real physical processes are discussed; a water bath temperature control system, an inverted pendulum, an electric vehicle generator Control system, and a multi-input multi-output furnace.
Journal Article
Study and Application of Combustion Model for Dual Fuel Engine
TL;DR: In this article, a combustion models of a methane-diesel dual fuel engine (DFE) are developed, which includes thermodynamic-kinetic combustion model, heat transfer and multi-zone combustion sub-model for pilot.