Feature extraction by non parametric mutual information maximization
The Journal of Machine Learning Research
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Efficient non-linear control through neuroevolution
ECML'06 Proceedings of the 17th European conference on Machine Learning
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Reinforcement learning based neural controllers for dynamic processes without exploration
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
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This paper presents the overall system of a learning, selforganizing, and adaptive controller used to optimize the combustion process in a hard-coal fired power plant. The system itself identifies relevant channels from the available measurements, classical process data and flame image information, and selects the most suited ones to learn a control strategy based on observed data. Due to the shifting nature of the process, the ability to re-adapt the whole system automatically is essential. The operation in a real power plant demonstrates the impact of this intelligent control system with its ability to increase efficiency and to reduce emissions of greenhouse gases much better then any previous control system.