Fuzzy Modeling by Data- Extracted Fuzzy Rules of Cement Rotary Kiln Based on WM Method

This research applys a fuzzy modeling method based on sampling data to the modeling of cement rotary kiln. By analyzing the sampling data in the actual production process, the mathematical model of the cement rotary kiln calcining system is established using the WM fuzzy modeling algorithm. The modeling method realizes the identification and optimization of the fuzzy rules. The simulation analysis is carried out through the Matlab software. The results show that this method has a high precision. It provides a reference to establish the system's mathematical models.

[1]  George W. Irwin,et al.  A fast method for fuzzy neural network modelling and refinement , 2009, Int. J. Model. Identif. Control..

[2]  Shaolin Wang,et al.  The Design and Implementation of a Cement kiln Expert System , 2007, 2007 IEEE International Conference on Automation and Logistics.

[3]  M. Shariat Panahi,et al.  Simulating the mechanical behavior of a rotary cement kiln using artificial neural networks , 2009 .

[4]  Alexander Bazhanov,et al.  Application of the model based on fuzzy behavior charts in the advising control system of rotary cement kiln , 2016, 2016 International Conference on Information and Digital Technologies (IDT).

[5]  Serge Guillaume,et al.  Designing fuzzy inference systems from data: An interpretability-oriented review , 2001, IEEE Trans. Fuzzy Syst..

[6]  Li-Xin Wang,et al.  The WM method completed: a flexible fuzzy system approach to data mining , 2003, IEEE Trans. Fuzzy Syst..

[7]  Gurunathan Saravana Kumar,et al.  Simulation based expert system to predict the deep drawing behaviour of tailor welded blanks , 2012, Int. J. Model. Identif. Control..

[8]  Meng Yuan,et al.  Intelligent dynamic modeling for online estimation of burning zone temperature in cement rotary kiln , 2014, Proceeding of the 11th World Congress on Intelligent Control and Automation.

[9]  Huajing Fang,et al.  Stability analysis of networked control system based on quasi T-S fuzzy model , 2012, Int. J. Model. Identif. Control..

[10]  Zhigang Li,et al.  A New Clustering Analysis Used for Generating Fuzzy Control Rules , 2008, 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery.

[11]  Tang Zhaohui.  Acquirement control rules of fuzzy expert control system in the progress of intelligent control , 2007 .