Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Artificial Intelligence Illuminated
Artificial Intelligence Illuminated
Expert Systems with Applications: An International Journal
Predictive models for emission of hydrogen powered car using various artificial intelligent tools
Neural Computing and Applications
Diesel engine emissions prediction using parallel neural networks
ACC'09 Proceedings of the 2009 conference on American Control Conference
Adaptive neural network model based predictive control for air-fuel ratio of SI engines
Engineering Applications of Artificial Intelligence
Prediction of diesel engine performance using biofuels with artificial neural network
Expert Systems with Applications: An International Journal
Emissions predictive modelling by investigating various neural network models
Expert Systems with Applications: An International Journal
An accelerated learning algorithm for multilayer perceptrons: optimization layer by layer
IEEE Transactions on Neural Networks
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This paper presents a comparison of predictive models for the estimation of engine power and tailpipe emissions for a 4kW gasoline scooter. This study forms a benchmark toward establishing an online emissions control and monitoring system to bring the emissions to within specific limits. Three emissions predictive models were investigated in this study; direct and series artificial neural network (ANN) models and a MATLAB dynamic model. The direct models takes variables lambda, throttle position, engine and vehicle speed to predict the engine power and the emissions CO, CO"2 and HC. The series model first takes the mentioned input to predict the engine power and consequently using the engine power as the fifth input to predict the emissions. For the ANN models, two multilayered networks were compared and analyzed; the backpropagation (BP) and optimization layer-by-layer (OLL) algorithms. The predictive accuracy for each algorithm were compared and it was found that the OLL network is the most accurate with a maximum mean relative error (MRE) of 1.78% and 1.38% for the direct and series predictive model respectively. Comparative results showed that the series neural network model gives the most accurate predictions, with MRE of 0.63% and 0.47% for the engine power and emissions respectively. The series neural network model can be seen as generic virtual power and emissions sensors, substituting costly and cumbersome hardware. Simple obtainable process parameters together with the series neural network will contribute immensely in control and tuning of emissions for real-time vehicular applications.