Colored object detection using 5 dof robot arm based adaptive neuro-fuzzy method

Mujiarto, Mujiarto; Djohar, As'ari; Komaro, Mumu; Mohamed, Mohamad Afendee; Rahayu, Darmawan Setia; Sanjaya, W. S. Mada; Mamat, Mustafa; Sambas, Aceng; Subiyanto, Subiyanto

doi:10.11591/ijeecs.v13.i1.pp293-299

Cited by 16 publications

(7 citation statements)

References 22 publications

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“…The fuzzy logic-based systems are one of the most important applications of the fuzzy logic and fuzzy-set theory in soft computing and applied mathematics (Alhihi and Khosravi, 2018;Mujiarto et al, 2019;Trisnawan et al, 2018;Santika et al, 2018;Trisnawan et al, 2019). Reasoning using the Sugeno method is almost the same as Mamdani reasoning, it's just that the system output (concentrations) are not in the form of fuzzy sets, but in the form of constants or linear.…”

Section: Fuzzy Logic Sugeno Methodsmentioning

confidence: 99%

Optimization of Traffic Light Control Using Fuzzy Logic Sugeno Method

Kartikasari¹

2020

ijgor

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Congestion is one of the big problems around the world, especially for big cities. Intersections are the scene of congestion because the lane is the meeting point of two or more roads which has a major influence on the smooth flow of vehicles on the road network. This congestion occurs due to various factors, one of which is the statistical traffic light duration, which does not match traffic conditions. Based on this, there needs to be a development in the timing of a more adaptive green light. This study describes the design of a traffic light controller using the Sugeno method fuzzy logic. This study aims to design a green light duration calculation by applying fuzzy logic that results in adaptive traffic light duration at intersections, by entering the density of each intersection path, which is divided into 4 inputs, namely regulated lane density, opposing lane density I, and opposite lane density. II, the density of the opposite lane III, with the aim of the system being able to produce a duration that is in accordance with the current traffic situation with an output in the form of a green light duration on the regulated lane.

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Section: Fuzzy Logic Sugeno Methodsmentioning

confidence: 99%

Optimization of Traffic Light Control Using Fuzzy Logic Sugeno Method

Kartikasari¹

2020

ijgor

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“…Fuzzy ID3 algorithm is an efficient algorithm to create a fuzzy decision tree. The algorithm of fuzzy ID3 is as follows (Liang, 2005;Mujiarto et al, 2019):…”

Section: Fuzzy Id3 Algorithmmentioning

confidence: 99%

Fuzzy Decision Tree to Predict Student Success in Their Studies

Santoso¹

2020

ijqrm

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The number of students graduating on time is one of the important aspects in the assessment of accreditation of a university. But the problem is still a lot of students who exceed the target time of graduation. Therefore, the prediction of graduation on time can serve as an early warning for the university management to prepare strategies related to the prevention of cases of drop out. The purpose of this research is to build a model using fuzzy decision tree to form the classification rules are used to predict the success of a student's study using fuzzy inference system. Results of this study was generated model of the number of classification rules are 28 rules when the value θr is 98% and θn is 3%, with the level of accuracy is 95.85%. Accuracy of Fuzzy ID3 algorithm is higher than ID3 algorithms in predicting the timely graduation of students.

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“…The adaptive network received much attention from various fields as a result of its enticing features such as speedy convergence, accurate learning, ease of use, tolerating uncertainties and imprecise information [17]. The learning algorithm for the fuzzy inference system (FIS) parameters updating can be back-propagation or hybrid learning algorithm [21][22][23].…”

Section: Hybrid Neuro Fuzzymentioning

confidence: 99%

Short term load forecast of Kano zone using artificial intelligent techniques

Imam

Gaya

Galadanci

2019

IJEECS

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<span lang="EN-MY">Load forecast provides useful information for effective electricity dispatch, planning for future expansion and significantly enhances operational efficiency. Conventional techniques yield unsatisfactory forecast which results in high energy losses and in turn leads to high operational cost and suppressed electricity demand. This paper presents hybrid neuro fuzzy (HNF) and Nonlinear Auto-Regressive with eXogeneous input (NARX) neural network for the short term load prediction of Kano region Nigeria. Simulation results obtained demonstrated the generalization capabilities of the models in predicting the load accurately well by achieving MAPE of 0.025% and 0.6551% for the HNF model and NARX network model respectively. The models could serve as promising tool for predicting Kano Zone load demand</span>.

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Colored object detection using 5 dof robot arm based adaptive neuro-fuzzy method

Cited by 16 publications

References 22 publications

Optimization of Traffic Light Control Using Fuzzy Logic Sugeno Method

Optimization of Traffic Light Control Using Fuzzy Logic Sugeno Method

Fuzzy Decision Tree to Predict Student Success in Their Studies

Short term load forecast of Kano zone using artificial intelligent techniques

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