Prediction of Tire Tractive Performance by Using Artificial Neural Networks

Çarman, Kazım; Taner, Alper

doi:10.3390/mca17030182

Cited by 13 publications

(12 citation statements)

References 20 publications

(20 reference statements)

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“…The relative error of the predicted value was found to be less (10%) than the acceptable limits (Taner, 2007;Çarman, Taner, 2012). In the regression model, the R 2 value was found to be 92.64% and the RMSE value was found to be 0.0962.…”

Section: Resultsmentioning

confidence: 72%

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Measurements and modelling of wind erosion rate in different tillage practices using a portable wind erosion tunnel

Çarman

Marakoğlu

Taner

et al. 2016

Zemdirbyste-Agriculture

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Artificial intelligence systems are widely accepted as a technology providing an alternative method to solve complex and ill-defined problems. Artificial neural network (ANN) is a technique with a flexible mathematical structure, which is capable of identifying a complex nonlinear relationship between the input and output data. The objective of this study was to investigate the relationship between dust concentration and wind erosion rate, and to illustrate how ANN might play an important role in the prediction of wind erosion rate. Data were recorded via field experiments by using a portable field wind tunnel. The experiments were carried out for eight different tillage applications that include the conventional, six different reduced tillage and the direct seeding practices. Particulate matter (PM) concentration generally decreased with a decrease in number or intensity of tillage operations. Direct seeding resulted in the lowest PM 10 concentration. After tillage applications, wind erosion rate varied between 113 and 1365 g m -2 h -1 . Results showed that wind erosion rate was lower in direct seeding than in conventional and reduced tillage applications. In this paper, a sophisticated intelligent model, based on a 1-(8-5)-1 ANN model with a back-propagation learning algorithm, was developed to predict the changes in the wind erosion rate due to dust concentration occurring during tillage. In addition, the prediction of the model was made according to traditional methods of wind erosion rate by using the programme Statistica, version 5. The verification of the proposed model was carried out by applying various numerical error criteria. The ANN model consistently provided better predictions compared with the nonlinear regression-based model. The relative error of the predicted values was found to be less than the acceptable limits (10%). Based on the results of this study, ANN appears to be a promising technique for predicting wind erosion rate.

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Section: Resultsmentioning

confidence: 72%

“…Measurements and modelling of wind erosion rate in different tillage practices using a portable wind erosion tunnel calculated using the following equations (Kashaninejad et al, 2009;Çarman, Taner, 2012):…”

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confidence: 99%

Measurements and modelling of wind erosion rate in different tillage practices using a portable wind erosion tunnel

Çarman

Marakoğlu

Taner

et al. 2016

Zemdirbyste-Agriculture

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“…The values calculated from ANN model according to the experimental values, average error of WMOB, SOB and ED was found as 0.32, 0.30 and 0.34 %, respectively. The average error was acceptable for all parameters that were obtained under the limit (10 %) [39].…”

Section: Artificial Neural Network (Ann) Resultsmentioning

confidence: 95%

Prediction of Kinematic Viscosities of Biodiesels Derived from Edible and Non-edible Vegetable Oils by Using Artificial Neural Networks

et al. 2015

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In the present study, the seeds named as wild mustard (Sinapis arvensis L.) and safflower (Carthamus tinctorius L.) were used as feedstocks for production of biodiesels. In order to obtain wild mustard seed oil (WMO) and safflower seed oil (SO), screw press apparatus was used. wild mustard seed oil biodiesel (WMOB) and safflower seed oil biodiesel (SOB) were produced using methanol and NaOH by transesterification process. Various properties of these biodiesels such as density (883.62-886.35 kg m −3 ), specific gravity (0.88442-0.88709), kinematic viscosity (5.75-4.11 mm 2 s −1 ), calorific value (40.63-38.97 MJ kg −1 ), flash point (171-175 • C), water content (328.19-412.15 mg kg −1 ), color (2.0-1.8), cloud point [5.8-(−4.7) • C], pour point [(-3.1)-(-13.1) • C), cold filter plugging point [(−2.0)-(−9.0) • C)], copper strip corrosion (1a-1a) and pH (7.831-7.037) were determined. Furthermore, kinematic viscosities of biodiesels and euro-diesel (ED) were measured at 298.15-373.15 K intervals with 1 K increments. Four different equations were used to predict the viscosities of fuels. Regression analyses were done in MATLAB program, and R 2 , correlation constants and root-mean-square error were determined. 1-7-7-3 artificial neural network (ANN) model with a back propagation learning algorithm was developed to predict the viscosities of fuels. The performance of neural B Tanzer Eryilmaz network-based model was compared with the performance of viscosity prediction models using same observed data. It was found that ANN model consistently gave better predictions (0.9999 R 2 values for all fuels) compared to these models. ANN model was showed 0.34 % maximum errors. Based on the results of this study, ANNs appear to be a promising technique for predicting viscosities of biodiesels.

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“…The mean relative error of measured and predicted values were 4.029 % for chickpea and 5.611 % for dry bean. For chickpea and dry bean seeds, the relative error of predicted value was found to be less than the acceptable limits (10%) (Çarman and Taner, 2012). Roy et al, (2019) Projected area (cm 2 ) Soylu et.…”

Section: Figurementioning

confidence: 92%

Fuzzy Knowledge-Based Model for Prediction of the Terminal Velocities of the Chickpea and Dry Bean Seeds

Soylu

Çarman²,

Çıtıl

2020

SJAFS

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Aerodynamic properties of solid materials have long been used to convey and separate seeds during harvest and postharvest operations. In this study, the terminal velocities of chickpea (Cicer arietinum L.) and dry bean (Phaseolus vulgaris L.) seeds as a function of seed mass and projected area were determined and also it was predicted by the fuzzy knowledge-based model. The results showed that the terminal velocity increased non linearly from 6.46 to 7.567m s-1 for chickpea and from 5.224 to 6.463 m s-1 for dry bean with an increase in seed mass and projected area. In this paper, a sophisticated intelligent model, based on Mamdani approach fuzzy modeling principles, was developed to predict the terminal velocities of chickpea and dry bean seeds. The verification of the proposed model is achieved via various numerical error criteria. The relative error of predicted values was found to be less than the acceptable limits (10%).

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Prediction of Tire Tractive Performance by Using Artificial Neural Networks

Cited by 13 publications

References 20 publications

Measurements and modelling of wind erosion rate in different tillage practices using a portable wind erosion tunnel

Measurements and modelling of wind erosion rate in different tillage practices using a portable wind erosion tunnel

Prediction of Kinematic Viscosities of Biodiesels Derived from Edible and Non-edible Vegetable Oils by Using Artificial Neural Networks

Fuzzy Knowledge-Based Model for Prediction of the Terminal Velocities of the Chickpea and Dry Bean Seeds

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