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

Shafaei, S.M.; Loghavi, M.; Kamgar, S.

doi:10.1016/j.compag.2017.09.023

Cited by 41 publications

(17 citation statements)

References 30 publications

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“…The chisel plow is commonly used for primary tillage operations with minimum soil dispersion, especially for farms having crop residue on the soil surface [18]. It helps prevent wind erosion, water runoff, and promoting water infiltration by breaking soil layers below normal tillage depth [19].…”

Section: Introductionmentioning

confidence: 99%

Effect of Soil Moisture, Tillage Speed, Depth, Ballast Weight and, Used Implement on Wheel Slippage of the Tractor: A Review

Mamkagh¹

2019

AJAAR

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Tractor wheel slippage is a critical parameter for fuel consumption and field performance and should not exceed 15%. Several attempts have been made to study the wheel slippage of the agricultural tractor in order to minimize it to acceptable levels during the tillage operations. There are many different types of implements for soil tillage, each one of them affects the wheel slippage in a different way. Moreover, several studies have found many operating conditions that can affect the wheel slippage significantly such as: soil moisture content, tillage speed, ballast weights and the type of implement used for tillage. This article reviews the relationship between them which gives possibility for further research to focus on the potential solutions to decrease the tractor driving wheel slippage which can positively affect the fuel consumption. Increase the additional mass of the tractor, decrease the air pressure in the tires, avoid tilling the soil that is too wet or too dry and, choose the right implement, speed and depth can decrease the tractor driving wheel slippage.

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Section: Introductionmentioning

confidence: 99%

Effect of Soil Moisture, Tillage Speed, Depth, Ballast Weight and, Used Implement on Wheel Slippage of the Tractor: A Review

Mamkagh¹

2019

AJAAR

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“…Fuzzy logic has an advantage in modeled the qualitative aspects of human knowledge and decision-making process by implementing a rule basis. Neural network has an advantage in recognizing a pattern, learning and practicing to solve a problem without requiring a mathematical model [13]. The fuzzy inference systems use the Takagi Sugeno Kang's (JSK) fuzzy inference system order one by considering simplicity and computation easiness.…”

Section: Related Workmentioning

confidence: 99%

Plant Seeds Growth Prediction on Greenhouse Using Adaptive Neuro Fuzzy Inference System (ANFIS) Method

2020

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Food security is a problem that every country had, especially for poor and developing countries. To improve the food security one of the solutions that can be applied is to collaborate technology and agriculture such as greenhouse. The technology that is applied to greenhouse can produce plants with good quality. Good quality plant can be predicted with prediction on the plant seeds in order to develop the plants production just as we expected. Prediction on plant seeds is using the adaptive neuro fuzzy inference system (ANFIS) model which is a combination of fuzzy and neural network. ANFIS will process the data with high complexity and it will provide the prediction result with high accuracy. Plant seeds prediction is using 65 data which divided into two data, specifically 50 training data and 15 testing data. The prediction provides accurate result and will generate 14/15 x 100% = 93.3333% precision with Mean Absolute Deviation (MAD) is 64.3391 from 15 prediction data about 4.2893, Mean Absolute Percentage Error (MAPE) is 5.3485 from 15 prediction data about 0.35657, Mean Square Deviation (MSD) is 9.159 from 15 prediction data about 0.6106.

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“…The ANFIS is a Takagi-Sugeno-Kang (TSK) type of fuzzy model proposed by Takagi-Sugeno-Kang (Takagi & Sugeno, 1985;Shafaei et al, 2017). It integrates both neural networks and fuzzy logic principles and it has the potential of capturing the benefits of both techniques into a single framework (Sampson et al, 2019).…”

Section: Adaptive Neuro-fuzzy Inference System Modulementioning

confidence: 99%

Neuro-Fuzzy Data Mining System for Identifying E-Commerce Related Threats

Haruna

Akinyede

Kehinde

2020

MJoC

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E-commerce is driven via Information Technology (IT) especially the web it mostly relies upon on innovative technologies that are facilitated by Electronic Data Interchange (EDI) and Electronic Payment over the web. Several researches have shown that e-commerce platforms are compromised by means of phishing and fraud attacks. This has necessitated trying to find innovative methodologies for defending e-commerce systems and users from the said threats. This research integrates Case Based Reasoning (CBR) and Adaptive Neuro-Fuzzy Inference System (ANFIS) to spot and categorise e-commerce websites transactions as legitimate or illegitimate by analyzing and evaluating some attributes. this may provide an invulnerable platforms for e-commerce users. The system, which was implemented on MATLAB, are often deployed on e-commerce systems and servers to watch e-commerce requests with the aim of identifying legitimate and illegitimate websites and transactions. The results of the implementation indicates that the machine is promising.

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

Cited by 41 publications

References 30 publications

Effect of Soil Moisture, Tillage Speed, Depth, Ballast Weight and, Used Implement on Wheel Slippage of the Tractor: A Review

Effect of Soil Moisture, Tillage Speed, Depth, Ballast Weight and, Used Implement on Wheel Slippage of the Tractor: A Review

Plant Seeds Growth Prediction on Greenhouse Using Adaptive Neuro Fuzzy Inference System (ANFIS) Method

Neuro-Fuzzy Data Mining System for Identifying E-Commerce Related Threats

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