Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Improved spiking neural networks for EEG classification and epilepsy and seizure detection
Integrated Computer-Aided Engineering
Probabilistic approach to NO and CO emission modeling
ACMOS'10 Proceedings of the 12th WSEAS international conference on Automatic control, modelling & simulation
Engineering Applications of Artificial Intelligence
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This paper presents a time delay neural network (TDNN) model designed for the prediction of nitrogen oxides (NOx ) and carbon monoxide (CO) emissions from a fossil fuel power plant. NOx and CO emissions of the plant are determined as a function of other related time-series such as air ow rates and oxygen levels that are measured during the system operation. Correlation analysis is performed on the data to determine the location and the spread of cross-correlation between pairs of variables and this information is used to form a variable tapped delay line at the input of the network. We also introduce a neural network based preprocessor which employs an iterative regularization scheme to recover missing portions of CO data that are censored due to saturation of the measuring device. Prediction after training with the restored data set is observed to be significantly more accurate.