Abstract
This paper presents a hybrid soft computing modeling approach, a neurofuzzy system based on rough set theory and genetic algorithms (GA). To solve the curse of dimensionality problem of neurofuzzy system, rough set is used to obtain the reductive fuzzy rule set. Both the number of condition attributes and rules are reduced. Genetic algorithm is used to obtain the optimal discretization of continuous attributes. The fuzzy system is then represented via an equivalent artificial neural network (ANN). Because the initial parameter of the ANN is reasonable, the convergence of the ANN training is fast. After the rules are reduced, the structure size of the ANN becomes small, and the ANN is not fully weight-connected. The neurofuzzy approach based on RST and GA has been applied to practical application of building a soft sensor model for estimating the freezing point of the light diesel fuel in fluid catalytic cracking unit.
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The authors are grateful to the financial support of the Chinese 863 project foundation.
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Luo, J.X., Shao, H.H. Developing soft sensors using hybrid soft computing methodology: a neurofuzzy system based on rough set theory and genetic algorithms. Soft Comput 10, 54–60 (2006). https://doi.org/10.1007/s00500-005-0465-0
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DOI: https://doi.org/10.1007/s00500-005-0465-0