【摘 要】
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This paper presents a hybrid soft computing modeling approach for a rough set theory and the genetic algorithms (NFRSGA). The fundamental problem of a neurofuzz
【机 构】
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Department of Automation,Institute of Automation
【基金项目】
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Sponsred by the National High Technology Research and Development Program of China
论文部分内容阅读
This paper presents a hybrid soft computing modeling approach for a rough set theory and the genetic algorithms (NFRSGA). The fundamental problem of a neurofuzzy system is that when the input dimension increases, the fuzzy rule base increases exponentially. This leads to a huge infra structure network which results in slow convergence. To solve this problem, rough set theory is used to obtain the reductive rules, which are used as fuzzy rules of the fuzzy system. The number of rules decrease, and each rule does not need all the conditional attribute values. This results in a reduced, or not fully connected, neural network. The structure of the neural network is relatively small and thus the weights to be trained decrease. The genetic algorithm is used to search the optimal discretization of the continuous attributes. The NFRSGA approach has been applied in the practical application of building a soft sensor model for estimating the freezing point of the light diesel fuel in a Fluid Catalytic Cracking Unit ( FCCU), and satisfying results are obtained.
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