Fuzzy modeling based on L/sub 2/ gain criterion

Author(s):  
T. Hori ◽  
T. Taniguti
Keyword(s):  
Author(s):  
G.I. Sainz Palmero ◽  
L. A. Campillo Quijano ◽  
M.J. Fuente
Keyword(s):  

2010 ◽  
Vol 36 (3) ◽  
pp. 412-420 ◽  
Author(s):  
Yong-Fu WANG ◽  
Dian-Hui WANG ◽  
Tian-You CHAI

2009 ◽  
Vol 2 (1) ◽  
pp. 40-47
Author(s):  
Montasser Tahat ◽  
Hussien Al-Wedyan ◽  
Kudret Demirli ◽  
Saad Mutasher

2019 ◽  
Vol 39 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Dian Lourençoni ◽  
Tadayuki Yanagi Junior ◽  
Paulo G. de Abreu ◽  
Alessandro T. Campos ◽  
Silvia de N. M. Yanagi

2011 ◽  
Vol 486 ◽  
pp. 262-265
Author(s):  
Amit Kohli ◽  
Mudit Sood ◽  
Anhad Singh Chawla

The objective of the present work is to simulate surface roughness in Computer Numerical Controlled (CNC) machine by Fuzzy Modeling of AISI 1045 Steel. To develop the fuzzy model; cutting depth, feed rate and speed are taken as input process parameters. The predicted results are compared with reliable set of experimental data for the validation of fuzzy model. Based upon reliable set of experimental data by Response Surface Methodology twenty fuzzy controlled rules using triangular membership function are constructed. By intelligent model based design and control of CNC process parameters, we can enhance the product quality, decrease the product cost and maintain the competitive position of steel.


2021 ◽  
pp. 1-1
Author(s):  
Atrayee Gupta ◽  
Ankita Nag ◽  
Nandini Mukherjee

2021 ◽  
Author(s):  
Kazem Zare ◽  
Mokhtar Shasadeghi ◽  
Afshin Izadian ◽  
Taher Niknam ◽  
Mohammad Hassan Asemani

Author(s):  
Tareq Salameh ◽  
Polamarasetty P Kumar ◽  
Enas Taha Sayed ◽  
Mohammad Ali Abdelkareem ◽  
Hegazy Rezk ◽  
...  

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