Genetic Fuzzy Modeling of Supervisory Scheduling of Freight Rail Systems

2008 ◽  
pp. 161-180
Author(s):  
Francisco Mota Filho ◽  
Rodrigo Goncalves ◽  
Fernando Gomide
Keyword(s):  
Fuzzy Modeling ◽  
Rail Systems

Accurate-interpretable based fuzzy modeling of a DC motor

2007 ◽  
Author(s):  
G.I. Sainz Palmero ◽  
L. A. Campillo Quijano ◽  
M.J. Fuente
Keyword(s):  
Fuzzy Modeling ◽  
Dc Motor

Data Mining and Systems Theory Based Fuzzy Modeling and Compensation for Friction

2010 ◽  
Vol 36 (3) ◽  
pp. 412-420 ◽  
Author(s):  
Yong-Fu WANG ◽  
Dian-Hui WANG ◽  
Tian-You CHAI
Keyword(s):  
Data Mining ◽  
Systems Theory ◽  
Fuzzy Modeling

Fuzzy Modeling of Multi-Degree-of-Freedom Power Spectral Density Systems

2009 ◽  
Vol 2 (1) ◽  
pp. 40-47
Author(s):  
Montasser Tahat ◽  
Hussien Al-Wedyan ◽  
Kudret Demirli ◽  
Saad Mutasher
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Fuzzy Modeling ◽  
Degree Of Freedom ◽  
Power Spectral

scholarly journals PRODUCTIVE RESPONSES FROM BROILER CHICKENS RAISED IN DIFFERENT COMMERCIAL PRODUCTION SYSTEMS - PART I: FUZZY MODELING

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
Keyword(s):  
Broiler Chickens ◽  
Production Systems ◽  
Fuzzy Modeling ◽  
Commercial Production

scholarly journals Alternative Road Vehicles, Electric Rail Systems, Short Flights: An Environmental Comparison

2008 ◽  
Vol 2 (4) ◽  
pp. 236-241 ◽  
Author(s):  
Julien Matheys ◽  
Tim Festraets ◽  
Jean-Marc Timmermans ◽  
Nele Sergeant ◽  
Joeri Van Mierlo
Keyword(s):  
Road Vehicles ◽  
Rail Systems

Fuzzy Modeling to Simulate Surface Roughness in CNC Turning of AISI 1045 Steel

2011 ◽  
Vol 486 ◽  
pp. 262-265
Author(s):  
Amit Kohli ◽  
Mudit Sood ◽  
Anhad Singh Chawla
Keyword(s):  
Experimental Data ◽  
Surface Roughness ◽  
Process Parameters ◽  
Fuzzy Model ◽  
Fuzzy Modeling ◽  
Cnc Machine ◽  
Model Based ◽  
Aisi 1045 Steel ◽  
Aisi 1045 ◽  
1045 Steel

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.

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