Abstract
This paper considers processes with many inputs and outputs from different application areas. Some parts of the inputs are measurable and others are not because of the presence of stochastic environmental factors. This is the reason why processes of this kind operate under uncertainty. As some factors cannot be measured and reflected into the process model, data mining methods cannot be applied. The proposed approach which can be applied in this case is based on artificial intelligence methods[1].
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Vatchova, B., Gegov, A. (2017). Production Rule and Network Structure Models for Knowledge Extraction from Complex Processes Under Uncertainty. In: Sgurev, V., Yager, R., Kacprzyk, J., Atanassov, K. (eds) Recent Contributions in Intelligent Systems. Studies in Computational Intelligence, vol 657. Springer, Cham. https://doi.org/10.1007/978-3-319-41438-6_20
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DOI: https://doi.org/10.1007/978-3-319-41438-6_20
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