Abstract
As a result of slow perturbations, the mathematical model of an actual system is difficult to be accurate. So when optimizing large-scale industrial process, the mathematical model and the actual system does not match, that is model-actual difference. Large-scale industrial process optimization based on fuzzy model is an effective way of this issue. However, the optimization model is the process of establishing a non-linear programming model. So, differential evolution algorithm is studied this paper to solve the problems of large-scale industrial processes optimization based on fuzzy models. To solve model-actual differences is mainly to solve fuzzy nonlinear problems: firstly, differential evolution algorithm is studied for solving fuzzy nonlinear problems in this paper. Then the combination of fuzzy nonlinear problems and the interaction balance method coordinated approach of large-scale industrial processes is proposed. Last, simulation results show the validity of the method which this paper studies.
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Dakuo, H., Yuanyuan, Z., Lifeng, W., Hongrui, C. (2010). Research on Differential Evolution Algorithm Based on Interaction Balance Method for Large-Scale Industrial Processes of Fuzzy Model. In: Wang, F.L., Deng, H., Gao, Y., Lei, J. (eds) Artificial Intelligence and Computational Intelligence. AICI 2010. Lecture Notes in Computer Science(), vol 6319. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16530-6_21
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DOI: https://doi.org/10.1007/978-3-642-16530-6_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16529-0
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