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ICONIP
2008

Neural Network Regression for LHF Process Optimization

14 years 28 days ago
Neural Network Regression for LHF Process Optimization
We present a system for regression using MLP neural networks with hyperbolic tangent functions in the input, hidden and output layer. The activation functions in the input and output layer are adjusted during the network training to fit better the distribution of the underlying data, while the network weights are trained to fit desired input-output mapping. A non-gradient variable step size training algorithm is used since it proved effective for that kind of problems. Finally we present a practical implementation, the system found in the optimization of metallurgical processes.
Miroslaw Kordos
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where ICONIP
Authors Miroslaw Kordos
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