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Book Categories |
Program Committee | ||
Foreword | ||
A | Perspectives | |
Learning and Generalization Characteristics of the Random Vector Functional-Link Net | 3 | |
Artificial Neural Networks and Expert Systems in the Power System Operation Environment | 11 | |
A Utility Perspective on Neural Networks, Fuzzy Logic, and Artificial Intelligence | 15 | |
B | Neural Network Methodologies | |
Backpropagation and Its Applications | 21 | |
Using Flow Graph Interreciprocity to Relate Recurrent-Backpropagation and Backpropagation-Through-Time | 31 | |
Neural Network Based Inferential Sensing and Instrumentation | 37 | |
Optimizing Neural Networks Using Genetic Algorithms | 41 | |
C | Nuclear Power Plants | |
Potential Use of Neural Networks in Nuclear Power Plants | 47 | |
Sensor Validation in Power Plants Using Neural Networks | 51 | |
Measuring Fuzzy Variables in a Nuclear Reactor Using Artificial Neural Networks | 55 | |
Application of a Real Time Artificial Neural Network for Classifying Nuclear Power Plant Transient Events | 59 | |
Control Rod Wear Recognition Using Neural Nets | 63 | |
Severe Accident Management System On-Line Network (SAMSON) | 69 | |
D | Power System Operation | |
Comparison of Dynamic Load Models Extrapolation Using Neural Networks and Traditional Methods | 77 | |
On Neural Network Voltage Assessment | 81 | |
Neural Network Synthesis of Tangent Hypersurfaces for Transient Security Assessment of Electric Power Systems | 87 | |
Power System Static Security Assessment Using the Kohonen Neural Network Classifier | 93 | |
Voltage Stability Monitoring with Artificial Neural Networks | 101 | |
Intelligent Load Shedding | 107 | |
Considerations in Intelligent Alarm Processing | 111 | |
E | Modeling and Prediction | |
Predictive Security Monitoring with Neural Networks | 117 | |
Empirical Modeling in Power Engineering Using the Recurrent Multilayer Perceptron Network | 123 | |
Modeling and Identification with Neural Networks | 129 | |
Autoregressive Neural Network Prediction: Learning Chaotic Time Series and Attractors | 135 | |
F | Control | |
Neural Control Systems | 143 | |
Potential Uses of Intelligent and Adaptive Controls for Electric Power System Operations in the Year 2000 and Beyond | 149 | |
Load-Frequency Control Using Neural Networks | 153 | |
Reinforcement Learning for Adaptive Control | 159 | |
G | Load Forecasting | |
Application of Artificial Neural Networks to Load Forecasting | 165 | |
Short-Term Electric Load Forecasting Using Neural Networks | 173 | |
Load Forecasting by Hierarchical Neural Networks that Incorporate Known Load Characteristics | 179 | |
H | Scheduling and Optimization | |
A Solution Method for Maintenance Scheduling of Thermal Units by Artificial Neural Networks | 185 | |
Generation Dispatch Algorithm Coordinating Economy and Stability by Using Artificial Neural Netoworks | 191 | |
I | Fault Diagnosis | |
Impulse Test Fault Diagnosis on Power Transformers Using Kohonen's Self-Organizing Neural Network | 199 | |
A Case Study of Neural Network Application: Power Equipment Application Failure | 207 | |
Integrating Neural Networks with Influence Diagrams for Power Plant Monitoring and Diagnostics | 213 | |
Use of Neural Network in Optimizing RPV Bolting Procedures | 217 | |
1993 INNS Board of Governors | ||
INNS Fact Sheet | ||
1993 INNS Membership Application |
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Add Neural Network Computing for the Electric Power Industry: Proceedings of the 1992 INNS Summer Workshop, Power system computing with neural networks is one of the fastest growing fields in the history of power system engineering. Since 1988, a considerable amount of work has been done in investigating computing capabilities of neural networks and understandi, Neural Network Computing for the Electric Power Industry: Proceedings of the 1992 INNS Summer Workshop to the inventory that you are selling on WonderClubX
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Add Neural Network Computing for the Electric Power Industry: Proceedings of the 1992 INNS Summer Workshop, Power system computing with neural networks is one of the fastest growing fields in the history of power system engineering. Since 1988, a considerable amount of work has been done in investigating computing capabilities of neural networks and understandi, Neural Network Computing for the Electric Power Industry: Proceedings of the 1992 INNS Summer Workshop to your collection on WonderClub |