Appraisal of Takagi-Sugeno-Kang type of adaptive neuro-fuzzy inference system for draft force prediction of chisel plow implement

Appraisal of Takagi-Sugeno-Kang type of adaptive neuro-fuzzy inference system for draft force... Computers and Electronics in Agriculture 142 (2017) 406–415 Contents lists available at ScienceDirect Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag Original papers Appraisal of Takagi-Sugeno-Kang type of adaptive neuro-fuzzy inference MARK system for draft force prediction of chisel plow implement S.M. Shafaei, M. Loghavi, S. Kamgar Department of Biosystems Engineering, School of Agriculture, Shiraz University, Shiraz 71441-65186, Iran ARTICLE I NFO ABSTRACT Keywords: Required draft force of chisel plow implement during tillage operations was comprehensively apprised. Field Soft computing method experiments were carried out at three levels of plowing depth (PD) (10, 20 and 30 (cm)) and three levels of Forward speed forward speed (FS) (2, 4 and 6 (km/h)) in a clay loam soil. An intelligent model based on soft computing Plowing depth technique, adaptive neuro-fuzzy inference system (ANFIS), was used to integrally predict draft force. The FS and Single effect PD were chosen as input variables and the draft force was considered as output parameter in the first order Dual interaction effect Takagi-Sugeno-Kang type of ANFIS model. A comparison was also performed between results of the best de- veloped ANFIS model and those of the well-known mathematical model suggested by American Society of Agricultural and Biological http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Computers and Electronics in Agriculture Elsevier

Appraisal of Takagi-Sugeno-Kang type of adaptive neuro-fuzzy inference system for draft force prediction of chisel plow implement

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Publisher
Elsevier
Copyright
Copyright © 2017 Elsevier B.V.
ISSN
0168-1699
eISSN
1872-7107
D.O.I.
10.1016/j.compag.2017.09.023
Publisher site
See Article on Publisher Site

Abstract

Computers and Electronics in Agriculture 142 (2017) 406–415 Contents lists available at ScienceDirect Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag Original papers Appraisal of Takagi-Sugeno-Kang type of adaptive neuro-fuzzy inference MARK system for draft force prediction of chisel plow implement S.M. Shafaei, M. Loghavi, S. Kamgar Department of Biosystems Engineering, School of Agriculture, Shiraz University, Shiraz 71441-65186, Iran ARTICLE I NFO ABSTRACT Keywords: Required draft force of chisel plow implement during tillage operations was comprehensively apprised. Field Soft computing method experiments were carried out at three levels of plowing depth (PD) (10, 20 and 30 (cm)) and three levels of Forward speed forward speed (FS) (2, 4 and 6 (km/h)) in a clay loam soil. An intelligent model based on soft computing Plowing depth technique, adaptive neuro-fuzzy inference system (ANFIS), was used to integrally predict draft force. The FS and Single effect PD were chosen as input variables and the draft force was considered as output parameter in the first order Dual interaction effect Takagi-Sugeno-Kang type of ANFIS model. A comparison was also performed between results of the best de- veloped ANFIS model and those of the well-known mathematical model suggested by American Society of Agricultural and Biological

Journal

Computers and Electronics in AgricultureElsevier

Published: Nov 1, 2017

References

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