Foundations of neural networks
Foundations of neural networks
Introduction to artificial neural systems
Introduction to artificial neural systems
Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Artificial Intelligence: A Guide to Intelligent Systems
Artificial Intelligence: A Guide to Intelligent Systems
Understanding Neural Networks; Computer Explorations
Understanding Neural Networks; Computer Explorations
Handbook of Neural Computing Applications
Handbook of Neural Computing Applications
Expert Systems with Applications: An International Journal
Contingency evaluation and monitorization using artificial neural networks
Neural Computing and Applications
An accelerated learning algorithm for multilayer perceptrons: optimization layer by layer
IEEE Transactions on Neural Networks
Intelligent control of braking process
Expert Systems with Applications: An International Journal
Comparative analysis of artificial neural networks and dynamic models as virtual sensors
Applied Soft Computing
| Hi-index | 12.05 |
This paper presents a two-stage emissions predictive model developed by investigating common feedforward neural network models. The first stage model involves predicting engine parameters power and tractive forces and the predicted parameters are used as inputs to the second stage model to predict the vehicle emissions. The following gasses were predicted from the tailpipe emissions for a scooter application; CO, CO"2, HC and O"2. Three feedforward neural network models were investigated and compared in this study; backpropagation, optimization layer-by-layer and radial basis function networks. Based on the experimental setup, the neural network models were trained and tested to accurately predict the effect of the engine operating conditions on the emissions by varying the number of hidden nodes. The selected optimization layer-by-layer network proved to be the most accurate and reliable predictive tool with prediction errors of +/-5%. The effect of the engine operating conditions on the tailpipe emissions for a scooter is shown to display similar qualitative and quantitative trends between the simulated and the experimental data. This study provides a better understanding in effects of engine process parameters on tailpipe emissions for the scooter as well as for general vehicular applications.