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
Computers and Electronics in Agriculture – Elsevier
Published: Nov 1, 2017
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