Abstract
Design of reasonable membership functions (MFs) is a primary problem for the fuzzy modeling method. Considering the complex nonlinear characteristics of blast furnace gas (BFG) system in steel industry, a MFs learning method based on clustering analysis is proposed in this paper, where a multi-objective density clustering method is reported by combing the targets of the model accuracy, complexity and interpretability. In order to simplify the modeling process and fit the distribution characteristics of industrial data, a simple type of function is designed and the optimized clustering results are used for determining the parameters of fuzzy MFs. To verify the performance of the proposed method, the practical data coming from a steel plant are employed. The experiment results demonstrate that the MFs designed by the proposed method could effectively improve the accuracy, complexity and interpretability of the fuzzy model, which provide helpful information for the fuzzy modeling of BFG pipeline pressure.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
