Intelligent Traffic Light Control System at Two Intersections Using Adaptive Neuro-Fuzzy Inference System (ANFIS) Method

2018 ◽  
Author(s):  
Rizky Aryo Bayu Utomo ◽  
Diaz Angga Permana ◽  
Pranoto Hidaya Rusmin
Keyword(s):  
Control System ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Light Control ◽  
Traffic Light ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Traffic Light Control ◽  
Anfis Method

Optimalisasi Lampu Lalu Lintas dengan Fuzzy Logic

2013 ◽  
Vol 5 (2) ◽  
pp. 58-62 ◽  
Author(s):  
Adhitya Yoga Yudanto ◽  
Marvin Apriyadi ◽  
Kevin Sanjaya
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Inference ◽  
System Control ◽  
Light Control ◽  
Traffic Light ◽  
Inference System ◽  
Traffic Lights ◽  
The Road ◽  
Traffic Light Control

The traffic lights problem is already commonly found in large cities. The traffic lights are supposed to control the flow of the road, but sometimes causes a congestion. This happens because the distribution of the time are all the same for all lines, without seeing the condition of the density of each lane. There’s one effort that can be done to overcome this problem, is to create a traffic light control system. With this system, the congestion that occurs around the traffic lights can be reduced. This system is using fuzzy logic. Fuzzy logic is one of computer science that studies about the value of truth that worth a lot. For example, a air conditioning system control subway Sendai in Japan. As for making a traffic light control system, the author using Fuzzy Inference System (FIS) that already exist in the application of MATLAB R2013a with Mamdani method. Index Terms —fuzzy logic, traffic lights, MATLAB.

Gas-Liquid Phase Recirculation in Bubble Column Reactors: Development of a Hybrid Model Based on Local CFD – Adaptive Neuro-Fuzzy Inference System (ANFIS)

2019 ◽  
Vol 44 (1) ◽  
pp. 29-42 ◽  
Author(s):  
Mashallah Rezakazemi ◽  
Saeed Shirazian
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Bubble Column ◽  
Scale Up ◽  
Flow Simulation ◽  
Continuous Phase ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Anfis Method ◽  
Different Dimensions

Abstract The Euler–Euler method and soft computing methods are recently utilized for the purpose of bubbly flow simulation and evolution of the dispersed and continuous phase in a two-phase reactor. Joining computational fluid dynamics (CFD) to the adaptive neuro-fuzzy inference system (ANFIS) method can enable the researchers to avoid several runs for heavy numerical methods (multidimensional Euler–Euler) to optimize fluid conditions. This overview can also help the researchers to carefully analyze fluid conditions and categorize their huge number of data in their artificial neural network nodes and avoid a complex non-structure CFD mesh. In addition, it can provide a neural geometry without limitation of an increasing mesh number in the fluid domain. In this study, gas and liquid circulation were considered as one of the main CFD factors in the scale-up of reactors used as an output parameter for prediction tool (ANFIS method) in different dimensions. This study shows that a combination of ANFIS and CFD methods provides the non-discrete domain in various dimensions and makes a smart tool to locally predict multiphase flow. The integration of numerical calculation and smart methods also shows that there is a great agreement between CFD results and ANFIS output depending on different dimensions.

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

2019 ◽  
Vol 13 (1) ◽  
pp. 293
Author(s):  
Mujiarto Mujiarto ◽  
Asari Djohar ◽  
Mumu Komaro ◽  
Mohamad Afendee Mohamed ◽  
Darmawan Setia Rahayu ◽  
...  
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Kinematic Model ◽  
Inverse Kinematic ◽  
Robot Arm ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Color Detection ◽  
Anfis Method ◽  
Arduino Microcontroller

<p>In this paper, an Adaptive Neuro Fuzzy Inference System (ANFIS) based on Arduino microcontroller is applied to the dynamic model of 5 DoF Robot Arm presented. MATLAB is used to detect colored objects based on image processing. Adaptive Neuro Fuzzy Inference System (ANFIS) method is a method for controlling robotic arm based on color detection of camera object and inverse kinematic model of trained data. Finally, the ANFIS algorithm is implemented in the robot arm to select objects and pick up red objects with good accuracy.</p>

scholarly journals Implementation of Fuzzy Mamdani Method for Traffic Lights Smart City in Rangkasbitung, Lebak Regency, Banten Province (Case Study of the Traffic Light T-junction, Cibadak, By Pas Sukarno Hatta Street)

KOMTEKINFO ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 176-185
Author(s):  
Dentik Karyaningsih ◽  
Robby Rizky
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Traffic Congestion ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Light Control ◽  
Traffic Light ◽  
Traffic Lights ◽  
Traffic Jams ◽  
Traffic Light Control

Traffic jams are a common sight that can be seen in almost all major cities in Indonesia. One of them is in Rangkasbitung City, Lebak Regency. This happens because the number of vehicles continues to increase. The traffic light control system implemented in Indonesia is a static preset time because the time of each phase is predetermined. This type of control system is still not effective in overcoming traffic congestion, especially at certain peak traffic jams. By using the Mamdani fuzzy logic system, it is possible to implement the human mindset into a system. Some rules can be set out in the fuzzy logic controller. The purpose of this study is to design a traffic light control system using fuzzy inference that regulates traffic based on its density. The data used are observations made at the research site. The conclusion of this study is to explain that the fuzzy mamdani method can solve existing problems in traffic congestion in Rangkasbitung City, Lebak Regency, Banten Province

scholarly journals IMPLEMENTATION OF ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) METHOD ON RICE PRICE PREDICTION IN LUBUKLINGGAU CITY

2021 ◽  
Vol 4 (2) ◽  
pp. 260-269
Author(s):  
Zulfauzi - ◽  
Budi Santoso ◽  
M. Agus Syamsul Arifin ◽  
Siti Nuraisyah
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
City Area ◽  
Price Prediction ◽  
Neuro Fuzzy ◽  
Rice Price ◽  
The Future ◽  
Anfis Method ◽  
Rice Prices

The problem behind this research is the imbalance between the capacity offered and the capacity demanded by the community, resulting in uncontrolled rice prices, so it is necessary to predict rice price in the future to monitor the stability of rice prices in the Lubuklinggau City area. In this study, the Adaptive Neuro-Fuzzy Inference System (ANFIS) method was used to predict future rice prices. The sample used in this study is data on rice price in Lubuklinggau City from January 2016 to December 2020. The result of the prediction of rice price in the Lubuklinggau City area for the next five years. With the accuracy value in rice price predictions based on MSE training, numely 99,9037% and based on the MSE test that is 99,8784%. While the accuracy values of rice price predictions based on MAPE training and testing are 93,2997% and 88,2782%, respectively. For the accuracy value of rice price prediction result based on the MSE and MAPE values respectively namely 99,8935% and 92,9212%. It can be concluded that the ANFIS method is very effectively used for the process of predicting a price or value in the future

scholarly journals Prototipe Penilaian Kinerja Tenaga Ahli PT. Inacon Luhur Pertiwi Dengan Pendekatan Adaptive Neuro Fuzzy Inference System (ANFIS)

2019 ◽  
Vol 5 (1) ◽  
pp. 108-122
Author(s):  
Handa Gustiawan
Keyword(s):  
Neural Networks ◽  
Data Analysis ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Primary Data ◽  
Inference System ◽  
Analysis Process ◽  
Neuro Fuzzy ◽  
Anfis Method ◽  
Data Reading

Inacon Luhur pertiwi PT. as amanagement consulting firm in carrying outits work on the project PNPM Urban withcontract number HK.02.03 / NMC / IBRD /SATKER-PK / 007/2012 dated 10 May 2012.By carrying out quantitative researchmethods, using primary and secondary dataas samples. Primary data retrieved byconducting an observation as anobservation instrument of expertsperformance assessment. Secondary datawas collected by observing the data,reading, studying and quoting from the bookof literature, as well as the resources thatare closely related to this study. The dataobtained will be used for purposes ofdescriptive data analysis process by usingAdaptive Neuro Fuzzy Inference System(ANFIS). ANFIS method is a method thatuses neural networks to implement fuzzyinference system. Fuzzy inference systemused is the fuzzy inference system modelsTagaki-Sugeno-Kang (TSK) withconsideration of simplicity and easycomputation. The result of this research isthe prototipe of expert performanceevaluation which can be implemented atInacon Luhur Pertiwi PT.

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