Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a comparison of predictive models for the estimation of engine por 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 re 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 por and the emissions CO, CO"2 and HC. The series model first takes the mentioned input to predict the engine por and consequently using the engine por as the fifth input to predict the emissions. For the ANN models, two multilayered networks re compared and analyzed; the backpropagation (BP) and optimization layer-by-layer (OLL) algorithms. The predictive accuracy for each algorithm re 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 shod that the series neural network model gives the most accurate predictions, with MRE of 0.63% and 0.47% for the engine por and emissions respectively. The series neural network model can be seen as generic virtual por 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.
['Wai Kean Yap', 'Vishy Karri']
I am writing a research paper on a idea Comparative analysis of artificial neural networks and dynamic models as virtual sensors
[{"from": "human", "value": "I am writing a research paper on a idea Comparative analysis of artificial neural networks and dynamic models as virtual sensors"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a comparison of predictive models for the estimation of engine por 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 re 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 por and the emissions CO, CO\"2 and HC. The series model first takes the mentioned input to predict the engine por and consequently using the engine por as the fifth input to predict the emissions. For the ANN models, two multilayered networks re compared and analyzed; the backpropagation (BP) and optimization layer-by-layer (OLL) algorithms. The predictive accuracy for each algorithm re 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 shod that the series neural network model gives the most accurate predictions, with MRE of 0.63% and 0.47% for the engine por and emissions respectively. The series neural network model can be seen as generic virtual por 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."}]