scholarly journals Adaptive Neuro-Fuzzy Inference System For Forecasting Traffic Volume(Case Study Of National Road Km 41 Kamal)

Jurnal CIVILA ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 155
Author(s):  
Agustinus Angkoso ◽  
Muhammad Shofwan Donny Cahyono
Keyword(s):  
Fuzzy Inference ◽  
Road Traffic ◽  
Traffic Volume ◽  
Volume Data ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Traffic Impact ◽  
National Road ◽  
Error Percentage

The effect of the development of an infrastructure such as shopping centers, settlements, and so on is one study that is generally considered in the governance of a region. Typically, this construction has a major traffic impact. Combined with the effects of population growth, which is constantly growing every year, the flow of traffic is increasingly congested. This is because many of these people choose to own personal vehicles. Tremendous congestion would result from a road capacity that is not proportional to vehicle growth. A traffic analysis was conducted using road traffic volume data to prevent this. This research will attempt to survey a road on National road KM 41 Kamal and use the neuro fuzzy method to forecast traffic volume. The amount of traffic that will be studied is only motorcycles. The results show that with an error percentage of 16.0793%, neuro fuzzy can predict motorcycle traffic volume. It can be inferred from this that Neuro Fuzzy can forecast traffic volume on a road quite well.

An Integrated Data Mining and Simulation Solution

2010 ◽  
pp. 929-948
Author(s):  
Mouhib Alnoukari ◽  
Asim El Sheikh ◽  
Zaidoun Alzoabi
Keyword(s):  
Data Mining ◽  
Fuzzy Inference ◽  
Decision Support Tools ◽  
Market Demand ◽  
Inference System ◽  
Car Market ◽  
Neuro Fuzzy ◽  
Support Tools ◽  
Quality In Higher Education

Simulation and data mining can provide managers with decision support tools. However, the heart of data mining is knowledge discovery; as it enables skilled practitioners with the power to discover relevant objects and the relationships that exist between these objects, while simulation provides a vehicle to represent those objects and their relationships. In this chapter, the authors will propose an intelligent DSS framework based on data mining and simulation integration. The main output of this framework is the increase of knowledge. Two case studies will be presented, the first one on car market demand simulation. The simulation model was built using neural networks to get the first set of prediction results. Data mining methodology used named ANFIS (Adaptive Neuro-Fuzzy Inference System). The second case study will demonstrate how applying data mining and simulation in assuring quality in higher education

scholarly journals Expert System Models for Forecasting Forklifts Engagement in a Warehouse Loading Operation: A Case Study

2016 ◽  
Vol 28 (4) ◽  
pp. 393-401 ◽  
Author(s):  
Dejan Mirčetić ◽  
Nebojša Ralević ◽  
Svetlana Nikoličić ◽  
Marinko Maslarić ◽  
Đurđica Stojanović
Keyword(s):  
Expert System ◽  
Customer Service ◽  
Fuzzy Inference ◽  
Service Level ◽  
Inference System ◽  
New Approach ◽  
Neuro Fuzzy ◽  
Customer Service Level ◽  
Distribution Chain

The paper focuses on the problem of forklifts engagement in warehouse loading operations. Two expert system (ES) models are created using several machine learning (ML) models. Models try to mimic expert decisions while determining the forklifts engagement in the loading operation. Different ML models are evaluated and adaptive neuro fuzzy inference system (ANFIS) and classification and regression trees (CART) are chosen as the ones which have shown best results for the research purpose. As a case study, a central warehouse of a beverage company was used. In a beverage distribution chain, the proper engagement of forklifts in a loading operation is crucial for maintaining the defined customer service level. The created ES models represent a new approach for the rationalization of the forklifts usage, particularly for solving the problem of the forklifts engagement incargo loading. They are simple, easy to understand, reliable, and practically applicable tool for deciding on the engagement of the forklifts in a loading operation.

An Integrated Data Mining and Simulation Solution

2010 ◽  
pp. 359-378 ◽  
Author(s):  
Mouhib Alnoukari ◽  
Asim El Sheikh ◽  
Zaidoun Alzoabi
Keyword(s):  
Data Mining ◽  
Fuzzy Inference ◽  
Decision Support Tools ◽  
Market Demand ◽  
Inference System ◽  
Car Market ◽  
Neuro Fuzzy ◽  
Support Tools ◽  
Quality In Higher Education

Simulation and data mining can provide managers with decision support tools. However, the heart of data mining is knowledge discovery; as it enables skilled practitioners with the power to discover relevant objects and the relationships that exist between these objects, while simulation provides a vehicle to represent those objects and their relationships. In this chapter, the authors will propose an intelligent DSS framework based on data mining and simulation integration. The main output of this framework is the increase of knowledge. Two case studies will be presented, the first one on car market demand simulation. The simulation model was built using neural networks to get the first set of prediction results. Data mining methodology used named ANFIS (Adaptive Neuro-Fuzzy Inference System). The second case study will demonstrate how applying data mining and simulation in assuring quality in higher education

Adaptive Neuro-Fuzzy Inference System in Agriculture

2020 ◽  
pp. 130-153
Author(s):  
K. Aditya Shastry ◽  
Sanjay H. A.
Keyword(s):  
Crop Yield ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Yield Prediction ◽  
Inference System ◽  
Fourth Section ◽  
The Third ◽  
Neuro Fuzzy ◽  
Basic Concepts

This chapter emphasizes the use of adaptive fuzzy inference system (ANFIS) in agriculture. An overview of the basic concepts of ANFIS is provided at the beginning, where the underlying architecture of ANFIS is also discussed. The introduction is followed by the second section which highlights the diverse applications of ANFIS in agriculture during recent times. The third section describes how Matlab software can be utilized to build the ANFIS model. The fourth section describes the case study of the application of ANFIS for crop yield prediction. The conclusion follows this case study.

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