Predictive models for emission of hydrogen powered car using various artificial intelligent tools

This paper investigates the use of artificial intelligent models as virtual sensors to predict relevant emissions such as carbon dioxide, carbon monoxide, unburnt hydrocarbons and oxides of nitrogen for a hydrogen powered car. The virtual sensors are developed by means of application of various Arti...

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Vydáno v:	Neural computing & applications Ročník 18; číslo 5; s. 469 - 476
Hlavní autoři:	Karri, Vishy, Ho, Tien Nhut
Médium:	Journal Article Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	London Springer-Verlag 01.06.2009 Springer
Témata:	Applied sciences Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Computer science; control theory; systems Data Mining and Knowledge Discovery Exact sciences and technology Image Processing and Computer Vision Inference from stochastic processes; time series analysis Isnn 2008 Learning and adaptive systems Mathematics Parametric inference Probability and statistics Probability and Statistics in Computer Science Sciences and techniques of general use Statistics Artificial intelligent techniques Back-propagation neural networks with Levenberg–Marquardt algorithm UTAS artificial neural networks Hydrogen powered car Hydrogen emission prediction Adaptive neuro-fuzzy inference systems Neural computation Fuzzy system Temperature Adaptive algorithm Error estimation Propagation Adaptive system Prediction Prediction theory Statistical estimation Neural network Mean estimation Intelligent sensors Mean square error Fuzzy algorithm Software Mean error Application
ISSN:	0941-0643, 1433-3058
On-line přístup:	Získat plný text
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