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Electrical and Computer Engineering Commons

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Series

1998

Learning (Artificial Intelligence)

Articles 1 - 2 of 2

Full-Text Articles in Electrical and Computer Engineering

Comparative Study Of Stock Trend Prediction Using Time Delay, Recurrent And Probabilistic Neural Networks, Donald C. Wunsch, Emad W. Saad, Danil V. Prokhorov Jan 1998

Comparative Study Of Stock Trend Prediction Using Time Delay, Recurrent And Probabilistic Neural Networks, Donald C. Wunsch, Emad W. Saad, Danil V. Prokhorov

Electrical and Computer Engineering Faculty Research & Creative Works

Three networks are compared for low false alarm stock trend predictions. Short-term trends, particularly attractive for neural network analysis, can be used profitably in scenarios such as option trading, but only with significant risk. Therefore, we focus on limiting false alarms, which improves the risk/reward ratio by preventing losses. To predict stock trends, we exploit time delay, recurrent, and probabilistic neural networks (TDNN, RNN, and PNN, respectively), utilizing conjugate gradient and multistream extended Kalman filter training for TDNN and RNN. We also discuss different predictability analysis techniques and perform an analysis of predictability based on a history of daily closing …


A Practical Continually Online Trained Artificial Neural Network Controller For A Turbogenerator, Ganesh K. Venayagamoorthy, Ronald G. Harley Jan 1998

A Practical Continually Online Trained Artificial Neural Network Controller For A Turbogenerator, Ganesh K. Venayagamoorthy, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

This paper reports on the simulation and practical studies carried out on a single turbogenerator connected to an infinite bus through a short transmission line, with a continually online trained (COT) artificial neural network (ANN) controller to identify the turbogenerator, and another COT ANN to control the turbogenerator. This identifier/controller augments/replaces the automatic voltage regulator and the turbine governor. Results are presented to show that this COT ANN identifier/controller has the potential to allow turbogenerators to operate more closely to their steady-state stability limits and nevertheless “ride through” severe transient disturbances such as three phase faults. This allows greater usage …