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- W1502970866 abstract "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." @default.
- W1502970866 created "2016-06-24" @default.
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- W1502970866 date "2009-01-01" @default.
- W1502970866 modified "2023-09-25" @default.
- W1502970866 title "Neural Network Regression for LHF Process Optimization" @default.
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- W1502970866 doi "https://doi.org/10.1007/978-3-642-03040-6_55" @default.
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