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Full-Text Articles in Engineering
Experimental Studies With Continually Online Trained Artificial Neural Network Identifiers For Multiple Turbogenerators On The Electric Power Grid, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley
Experimental Studies With Continually Online Trained Artificial Neural Network Identifiers For Multiple Turbogenerators On The Electric Power Grid, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
The increasing complexity of a modern power grid highlights the need for advanced system identification techniques for effective control of power systems. This paper provides a new method for nonlinear identification of turbogenerators in a 3-machine 6-bus power system using online trained feedforward neural networks. Each turbogenerator in the power system is equipped with a neuro-identifier, which is able to identify its particular turbogenerator and the rest of the network to which it is connected from moment to moment, based on only local measurements. Each neuro-identifier can then be used in the design of a nonlinear neurocontroller for each turbogenerator …
Excitation And Turbine Neurocontrol With Derivative Adaptive Critics Of Multiple Generators On The Power Grid, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley
Excitation And Turbine Neurocontrol With Derivative Adaptive Critics Of Multiple Generators On The Power Grid, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
Based on derivative adaptive critics, neurocontrollers for excitation and turbine control of multiple generators on the electric power grid are presented. The feedback variables are completely based on local measurements. Simulations on a three-machine power system demonstrate that the neurocontrollers are much more effective than conventional PID controllers, the automatic voltage regulators and the governors, for improving the dynamic performance and stability under small and large disturbances
A Committee Of Neural Networks For Automatic Speaker Recognition (Asr) Systems, Viresh Moonasar, Ganesh K. Venayagamoorthy
A Committee Of Neural Networks For Automatic Speaker Recognition (Asr) Systems, Viresh Moonasar, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
This paper describes how the results of speaker verification systems can be improved and made robust with the use of a committee of neural networks for pattern recognition rather than the conventional single-network decision system. It illustrates the use of a supervised learning vector quantization neural network as the pattern classifier. Linear predictive coding and cepstral signal processing techniques are utilized to form hybrid feature parameter vectors to combat the effect of decreased recognition success with increased group size (number of speakers to be recognized)
A Parallel Computer-Go Player, Using Hdp Method, Donald C. Wunsch, Xindi Cai
A Parallel Computer-Go Player, Using Hdp Method, Donald C. Wunsch, Xindi Cai
Electrical and Computer Engineering Faculty Research & Creative Works
The game of Go has simple rules to learn but requires complex strategies to play well, and, the conventional tree search algorithm for computer games is not suited for Go program. Thus, the game of Go is an ideal problem domain for machine learning algorithms. This paper examines the performance of a 19x19 computer Go player, using heuristic dynamic programming (HDP) and parallel alpha-beta search. The neural network based Go player learns good Go evaluation functions and wins about 30% of the games in a test series on 19x19 board