Hybrid fuzzy modelling using memetic algorithm for hydrocyclone control

The use of a hybrid fuzzy modeling can act as a good alternative in establishing a hydrocyclone control model in estimating the hydrocyclone parameter, d50c. In most control and engineering applications, the use of fuzzy system as a way to improve the human-computer interaction has becoming popular. The main advantage of this proposed hybrid fuzzy system used for hydrocyclone control is that it only presents a small amount of fuzzy rules. It uses memetic algorithms to optimize the fuzzy parameters of the system to yield in a more accurate hydrocyclone control system.

[1]  Pablo Moscato,et al.  On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts : Towards Memetic Algorithms , 1989 .

[2]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[3]  Halit Eren,et al.  An application of artificial neural network for prediction of densities and particle size distributions in mineral processing industry , 1997, IEEE Instrumentation and Measurement Technology Conference Sensing, Processing, Networking. IMTC Proceedings.

[4]  Tamás D. Gedeon,et al.  Fuzzy rule interpolation for multidimensional input spaces in determining d50c of hydrocyclones , 2003, IEEE Trans. Instrum. Meas..

[5]  László T. Kóczy,et al.  Improvements and critique on Sugeno's and Yasukawa's qualitative modeling , 2002, IEEE Trans. Fuzzy Syst..

[6]  Halit Eren,et al.  Use of artificial neural networks in estimation of Hydrocyclone parameters with unusual input variables , 1996, Quality Measurement: The Indispensable Bridge between Theory and Reality (No Measurements? No Science! Joint Conference - 1996: IEEE Instrumentation and Measurement Technology Conference and IMEKO Tec.

[7]  Kok Wai Wong,et al.  A self-generating fuzzy rules inference system for petrophysical properties prediction , 1997, 1997 IEEE International Conference on Intelligent Processing Systems (Cat. No.97TH8335).

[8]  Kok Wai Wong,et al.  Parameter identification using memetic algorithms for fuzzy systems , 2003 .

[9]  Michio Sugeno,et al.  A fuzzy-logic-based approach to qualitative modeling , 1993, IEEE Trans. Fuzzy Syst..

[10]  Halit Eren,et al.  Artificial neural networks in estimation of hydrocyclone parameter d50/sub c/ with unusual input variables , 1997 .

[11]  Kok Wai Wong,et al.  Notes on Sugeno and Yasukawa's fuzzy modelling approach , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[12]  Shigeo Abe,et al.  Function approximation based on fuzzy rules extracted from partitioned numerical data , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[13]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[14]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[15]  Halit Eren,et al.  Developing a generalised neural-fuzzy hydrocyclone model for particle separation , 1998, IMTC/98 Conference Proceedings. IEEE Instrumentation and Measurement Technology Conference. Where Instrumentation is Going (Cat. No.98CH36222).