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- W20508059 abstract "We model a part of a process in pulp to paper production using feed forward connected neural networks. A set of parameters related to paper quality is predicted from a set of process values. The predicted values are results from laboratory experiments which are time consuming. The number of training vectors were rather limited. Therefore, our work was focused on finding the relevant inputs for each signal and to find the architecture that was most efficient for each output. The output vector is separated into single values which are predicted on different architectures adapted to each output. A strategy that continuously adapts the process model seems to be useful. In this work the backprop learning algorithm has been used." @default.
- W20508059 created "2016-06-24" @default.
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- W20508059 date "1993-01-01" @default.
- W20508059 modified "2023-10-14" @default.
- W20508059 title "Process Modelling Using Artificial Neural Networks" @default.
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- W20508059 doi "https://doi.org/10.1007/978-1-4471-2063-6_248" @default.
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