Fuzzy knowledge-based model for prediction of soil loosening and draft efficiency in tillage

Marakoğlu, Tamer; Çarman, Kazım

doi:10.1016/j.jterra.2009.10.001

Cited by 38 publications

(23 citation statements)

References 13 publications

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“…The goodness of fit of the prediction values from the FES model on the tractive effort, total motion resistance and total power consumption was found to be 0.90, 0.98, and 0.97, respectively. All values were close to 1.0 as expected (Marakoglu and Carman, 2010).…”

Section: Resultssupporting

confidence: 84%

Fuzzy expert system for controlling swamp terrain intelligent air-cushion tracked vehicle

Hossain

Rahman

Mohiuddin

2011

Int.J Automot. Technol.

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A fuzzy expert system was used in this study to control an intelligent air-cushion tracked vehicle (IACTV) as it operated in a swamp peat terrain. The system was effective in controlling the intelligent air-cushion vehicle while measuring the vehicle traction (TE), motion resistance (MR), power consumption (PC), cushion clearance height (CCH) and cushion pressure (CP). An ultrasonic displacement sensor, pull-in solenoid electromagnetic switch, pressure-control sensor, microcontroller, and battery pH sensor were incorporated into the fuzzy expert system (FES) to experimentally determine the TE, MR, PC, CCH, and CP. In this study, we provide an illustration of how an FES might play an important role in the prediction of the power consumption of the vehicle's intelligent air-cushion system. The mean relative error in the actual and predicted values from the FES model with respect to tractive effort, total motion resistance and total power consumption were found to be 5.58 %, 6.78 % and 10.63 %, respectively. For all parameters, the relative error in the predicted values was found to be less than the acceptable limit (10%), except for the total power consumption. Furthermore, the goodness of fit of the predicted values was found to be close to 1.0 as expected and, hence, indicates the good performance of the developed system.

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Section: Resultssupporting

confidence: 84%

Fuzzy expert system for controlling swamp terrain intelligent air-cushion tracked vehicle

Hossain

Rahman

Mohiuddin

2011

Int.J Automot. Technol.

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“…Prediction of power consumption has been done by using the FLES model based on vehicle sinkage (VS) and vehicle weight (VW). The means of the measured and [22]. The goodness of fit of the values predicted from the FLES model was 0.964 which is close to 1.0 as expected.…”

Section: Power Consumption Prediction and Validationsupporting

confidence: 80%

“…The correlation coefficient was 0.971, which is significant in operation. Furthermore, for flowrate, the mean relative error of the actual value and predicted values from the FLES model was 10.93 per cent, which is almost equal to the acceptable limit of 10 per cent [22]. The goodness of fit of the values predicted from the FLES model was 0.91 which is close to 1.0 as expected.…”

Section: Investigation Of Control System Performance By Laboratory Tesupporting

confidence: 72%

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Nonlinear controller of an air-cushion system for a swamp terrain vehicle: fuzzy logic approach

Hossain

Rahman

Mohiuddin

2011

Proceedings of the Institution of Mechanical Engineers, Part D:

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This paper presents the fuzzy logic controller (FLC) of an air-cushion system for a swamp peat terrain vehicle and describes the process by which it functions. Cushion pressure is controlled by an electronic proportional control valve and FLC using the output signal of the distance (height) measuring sensor that was attached to the vehicle. The main purpose of this study was to develop a control scheme for an air-cushion system and to investigate the relationship between vehicle vertical position and the air-cushion system, and to illustrate the important role of the fuzzy logic control system. Experimental values were recorded in the laboratory for control system testing, and in the swamp peat terrain field for vehicle performance investigation. In this paper, a fuzzy logic expert system (FLES) model, based on the Mamdani approach, was developed to predict the changes in flowrate. The mean relative error of actual and predicted values from the FLES model of flowrate was found to be slightly above the acceptable limit. The goodness of fit of the prediction values from the FLES model was found to be close to 1.0 as expected, and hence demonstrated the good performance of the developed system.

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“…Accordingly, fuzzy modeling as a powerful tool has been used to evaluate different aspects of sustainable agriculture; i.e. farm machinery and tillage systems [35,36], fertilizer and herbicide application [37], energy utilization and sustainability [38][39][40], and postharvest technologies [41]. Nonetheless, a large number of studies on energy inputs for crop production have used a computational framework without any modeling [42,11,22,[43][44][45][46][47][48].…”

Section: Why Using Fuzzy Modeling and Dea Technique?mentioning

confidence: 99%

Sustainable and efficient energy consumption of corn production in Southwest Iran: Combination of multi-fuzzy and DEA modeling

Houshyar

Azadi

Almassi

et al. 2012

Energy

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Abstract:The goal of this study was to evaluate the sustainability and efficiency of corn production with regard to energy consumption in the Fars province, Southwest Iran. To reach the goal, fuzzy modeling and Data Envelopment Analysis (DEA) were employed. The fuzzy model included three sub-models which assessed the sustainability use of energy inputs, i.e. machinery, fuel, fertilizer, chemical, human power, seed, and electricity. Some DEA models like CCR (Charnes, Cooper and Rhodes), BCC (Banker, Charnes and Cooper) and SBM (Slack Based Measure) were applied in assessment of efficiency scores. Using non-hierarchical cluster analysis, 89 farmers were chosen in two clusters including 47 and 42 farmers as the first and second cluster, respectively. The farmers in cluster 2 used 44238.235 MJ/ha energy input which was 2500 MJ/ha more than the first cluster. The fuzzy modeling revealed that the sustainability indices were higher in the first cluster. However, the indices were in the ranges of very low to medium for all the farmers, representing that the current corn production system is not sustainable. Also, the DEA models revealed that the first cluster also held more efficient farmers. Since DEA defines a reference set of efficient for inefficient farmers to follow, lead inefficient farmers to improve their sustainability up to efficient ones. Therefore, efficient and inefficient farmers must change the trends of energy utilization for approaching even more sustainable production.

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Fuzzy knowledge-based model for prediction of soil loosening and draft efficiency in tillage

Cited by 38 publications

References 13 publications

Fuzzy expert system for controlling swamp terrain intelligent air-cushion tracked vehicle

Fuzzy expert system for controlling swamp terrain intelligent air-cushion tracked vehicle

Nonlinear controller of an air-cushion system for a swamp terrain vehicle: fuzzy logic approach

Sustainable and efficient energy consumption of corn production in Southwest Iran: Combination of multi-fuzzy and DEA modeling

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