scholarly journals Analysis of The Impact of Fuzzy Logic Algorithm On Handover Decision in A Cellular Network

2017 ◽  
Vol 5 (5) ◽  
pp. 46-62 ◽  
Author(s):  
Emily Teresa Nyambati ◽  
Vitalice K. Oduol
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Intelligent Systems ◽  
Fuzzy Inference ◽  
Inference System ◽  
Handover Decision ◽  
Fuzzy Logic Algorithm ◽  
Gsm Network ◽  
Work Done ◽  
The Impact

Fuzzy logic is one of the intelligent systems that can be used to develop algorithms for handover. For success in handing over, the decision-making process is crucial and thus should be highly considered. The performance of fixed parameters is not okay in the changing cellular system environments. The work done on this paper aims to analyse the impact of utilising the fuzzy logic system for handover decision making considering the Global System for Mobile communication (GSM) network. The results from the different simulations show that the need to handover varies depending on the input(s) to the Fuzzy Inference System (FIS). By increasing the number of data, thus the criteria parameters used in the algorithm, an Optimised Handover Decision (OHOD) is realised.

scholarly journals PENENTUAN KECEPATAN PUTAR KIPAS ANGIN DAN INTENSITAS LAMPU DENGAN FUZZY LOGIC MAMDANI

2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Chyquitha Danuputri
Keyword(s):  
Fuzzy Logic ◽  
Light Intensity ◽  
Intelligent Systems ◽  
Smart Home ◽  
Room Temperature ◽  
Fuzzy Inference ◽  
Inference System ◽  
Fuzzy Logic Algorithm ◽  
Temperature And Humidity ◽  
The Republic

<p><em>Intelligent systems are one of the most important branches of the computer world. Computers are expected to be able to solve various problems in the real world, not just a tool for doing calculations. To make this system, algorithms are needed that are in accordance with the problems faced so that they can solve or produce the decisions needed to solve these problems appropriately. Mamdani fuzzy logic algorithm is one of the algorithms that can be applied in intelligent systems. Fuzzzy mamdani algorithm, is one part of the Fuzzy Inference System which is useful for making the best conclusion or decision in an uncertain problem. This research focuses on the calculation of the fuzzy logic algorithm in providing answers to the uncertainties found in smart home systems used to control the speed of a fan and lights, while the factors that become uncertain in controlling a fan are room temperature and humidity and For lamps, they have a factor of light intensity and time of the region, for these factors, the researchers use the Humanity Guide Hygiene standard reference for humidity and the Regulation of the Minister of Health of the Republic of Indonesia Number 1077 / Menkes / Per / V / 2011 concerning Guidelines for Air Sanitation in Home Spaces. Through this research, it can be seen that using the mamdani fuzzy logic algorithm can provide a result in the form of a decision to determine how fast a fan should rotate based on the temperature and humidity factors in the room as well as the level of light intensity that the lights must emit.</em><strong><em></em></strong></p>

scholarly journals Aplikasi Pengambilan Keputusan dengan Metode Tsukamoto pada Penentuan Tingkat Kepuasan pelanggan

CAUCHY ◽  
2015 ◽  
Vol 4 (1) ◽  
pp. 10 ◽  
Author(s):  
Venny Riana Riana Agustin ◽  
Wahyu Henky Irawan
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Weighted Average ◽  
Fuzzy Rules ◽  
Quality Of Services ◽  
Inference System ◽  
A Value ◽  
Fuzzy Logic Analysis

Tsukamoto method is one method of fuzzy inference system on fuzzy logic for decision making. Steps of the decision making in this method, namely fuzzyfication (process changing the input into kabur), the establishment of fuzzy rules, fuzzy logic analysis, defuzzyfication (affirmation), as well as the conclusion and interpretation of the results. The results from this research are steps of the decision making in Tsukamoto method, namely fuzzyfication (process changing the input into kabur), the establishment of fuzzy rules by the general form IF a is A THEN B is B, fuzzy logic analysis to get alpha in every rule, defuzzyfication (affirmation) by weighted average method, as well as the conclusion and interpretation of the results. On customers at the case, in value of 16 the quality of services, the value of 17 the quality of goods, and value of 16 a price, a value of the results is 45,29063 and the level is low satisfaction

scholarly journals An Adaptive Handover Prediction Scheme for Seamless Mobility Based Wireless Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Ali Safa Sadiq ◽  
Norsheila Binti Fisal ◽  
Kayhan Zrar Ghafoor ◽  
Jaime Lloret
Keyword(s):  
Decision Making ◽  
Wireless Networks ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Weight Vector ◽  
Point Load ◽  
Fuzzy Decision Making ◽  
Inference System ◽  
Handover Decision ◽  
Seamless Mobility

We propose an adaptive handover prediction (AHP) scheme for seamless mobility based wireless networks. That is, the AHP scheme incorporates fuzzy logic with AP prediction process in order to lend cognitive capability to handover decision making. Selection metrics, including received signal strength, mobile node relative direction towards the access points in the vicinity, and access point load, are collected and considered inputs of the fuzzy decision making system in order to select the best preferable AP around WLANs. The obtained handover decision which is based on the calculated quality cost using fuzzy inference system is also based on adaptable coefficients instead of fixed coefficients. In other words, the mean and the standard deviation of the normalized network prediction metrics of fuzzy inference system, which are collected from available WLANs are obtained adaptively. Accordingly, they are applied as statistical information to adjust or adapt the coefficients of membership functions. In addition, we propose an adjustable weight vector concept for input metrics in order to cope with the continuous, unpredictable variation in their membership degrees. Furthermore, handover decisions are performed in each MN independently after knowing RSS, direction toward APs, and AP load. Finally, performance evaluation of the proposed scheme shows its superiority compared with representatives of the prediction approaches.

scholarly journals Hardware Simulation of Rear-End Collision Avoidance System Based on Fuzzy Logic

2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Noor Cholis Basjaruddin ◽  
Didin Saefudin ◽  
Anggun Pancawati
Keyword(s):  
Fuzzy Logic ◽  
Collision Avoidance ◽  
Fuzzy Inference ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Inference System ◽  
Fuzzy Logic Algorithm ◽  
Pile Up ◽  
Assistance Systems ◽  
Collision Avoidance System

Rear-end collisions are the most common type of traffc accident. On the highway, a real-end collision may involve more than two vehicles and cause a pile-up or chain-reaction crash. Referring to data released by the Australian Capital Territory (ACT), rear-end  collisions which occurred throughout 2010 constituted as much as 43.65% of all collisions. In most cases, these rear-end collisions are caused by inattentive drivers, adverse road conditions and poor following distance. The Rear-end Collision Avoidance System (RCAS) is a device to help drivers to avoid rear-end collisions. The RCAS is a subsystem of Advanced Driver Assistance Systems (ADASs) and became an important part of the driverless car. This paper discusses a hardware simulation of a RCAS based on fuzzy logic using a remote control car. The Mamdani method was used as a fuzzy inference system and realized by using the Arduiono Uno microcontroller system. Simulation results showed that the fuzzy logic algorithm of RCAS can work as designed.

The Effect of Neighboring Cells on Handover Decision Making Based on Fuzzy in the WCDMA Network

2017 ◽  
Vol 5 (2) ◽  
pp. 52-58
Author(s):  
Akbar Nur
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Base Station ◽  
Test Method ◽  
User Mobility ◽  
Handover Decision ◽  
Final Project ◽  
Soft Handover ◽  
Hard Handover ◽  
The Impact

In channel transfer (handover) from one Base Station to another Base Station. The purpose of this final project is to analyze the effect of neighboring cells on handover decisions on WCDMA networks based on fuzzy, in this handover process, handover decisions use several parameters related to handovers and supported by fuzzy logic. Relatively high user mobility demands a guarantee until the use of the service ends, the impact of user mobility results in the output being analyzed for this handover decision to help give consideration to the optimal handover decision. The method used is Tsukamoto fuzzy logic, for decision making, while the measurement method In the field, the drive test method is carried out by measuring the signal level around the base station area, and comparing the results of the two methods. Comparison of handover decisions between the results of fuzzy logic and measurements, for example for the results of no proper in fuzzy logic, yields a rate value of 0% for soft handovers and 100% for hard handovers, and for proper results in fuzzy logic, yields a rate value for measurement. 95.22% for soft / soft handover and 4.72% for hard handover

scholarly journals Fuzzy logic applied to different adaptability and stability methods in common bean

2020 ◽  
Vol 55 ◽  
Author(s):  
Anna Regina Tiago Carneiro ◽  
Demerson Arruda Sanglard ◽  
Alcinei Mistico Azevedo ◽  
Thiago Lívio Pessoa Oliveira de Souza ◽  
Helton Santos Pereira ◽  
...  
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Phaseolus Vulgaris ◽  
Common Bean ◽  
Fuzzy Inference ◽  
R Software ◽  
Inference System ◽  
Modified Method ◽  
The One

Abstract: The objective of this work was to evaluate the efficiency of the methods of Eberhart & Russell and Lin & Binns, modified for the automation of decision-making by fuzzy logic, in assessing the adaptability and stability of common bean (Phaseolus vulgaris) cultivars. Eighteen cultivars of the “carioca” commercial group were evaluated in 11 environments, in Brazil. All results were obtained by programming in the R software. The developed controllers were based on the fuzzy inference system developed by Mamdani. This system was modeled to enable interpretations of the method of Eberhart & Russell alone or together with the modified method of Lin & Binns. The controller based on Eberhart & Russell and the one based on Eberhart & Russell and Lin & Binns identified the same cultivars as having general adaptability, but differed as to the classification of cultivars adapted to unfavorable environments. The BRSMG Pioneiro, BRS Pontal, IAC-Carioca Tybatã, and IPR Juriti cultivars presented general adaptability, whereas Campeão, Pérola, and BRS Estilo showed specific adaptability to favorable environments. The fuzzy logic methods used are efficient and allow the classification of all evaluated cultivars.

Fuzzy Logic Modeling for Decision Making Processes Using MATLAB

2014 ◽  
Vol 984-985 ◽  
pp. 425-430 ◽  
Author(s):  
Thangavel Ramya ◽  
A.C. Kannan ◽  
R.S. Balasenthil ◽  
B. Anusuya Bagirathi
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Decision Making Process ◽  
Computing Environment ◽  
Output Variable ◽  
Inference System ◽  
Logic Modeling ◽  
Decision Making Processes

— This paper demonstrates to build a Fuzzy Inference System (FIS) for any model utilizing the Fuzzy Logic Toolbox graphical user interface (GUI) tools. A different conception for decision making process, based on the fuzzy approach, is propounded by authors of the paper.The paper is worked out in two sections. Description about the Fuzzy Logic Tool box is done in the first section.Illustration with an introductory example concludes the second section. Based on various assumptions the authors construct the rule statements which are then converted into fuzzy rules and the GUI tools of the Fuzzy Logic Toolbox built using MATLAB numeric computing environment is used to construct a fuzzy inference system for this process.The output membership functions are expected to be fuzzy sets in Mamdani-type inference.Defuzzification of fuzzy set for each output variable generated after the aggregation process has to be carried out. Application of information technology for Decisions in today's environment which is highly competitive are undeniable principles of organizations and helps managers in making useful decisions meaningfully.

scholarly journals Sistem Pendukung Keputusan Metode Sugeno dalam Menentukan Tingkat Kepribadian Siswa Berdasarkan Pendidikan

CAUCHY ◽  
2015 ◽  
Vol 4 (1) ◽  
pp. 48
Author(s):  
Wildan Hakim ◽  
Turmudi Turmudi
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Fuzzy Inference System ◽  
Informal Education ◽  
Formal Education ◽  
Fuzzy Inference ◽  
Zero Order ◽  
Composition Rule ◽  
Inference System ◽  
A Value

Sugeno method is one method of fuzzy inference system on fuzzy logic for decision making. In-Zero Order Sugeno method in fuzzy logic consists of four stages: 1. fuzzification. 2. The application functionality implications, the implication function used is function MIN (minimum). 3. The composition rule using the function MAX (maximum). 4. Defuzzification weight average. Based on case 1, each student with non-formal education and informal education by 12 by 19 has a value of 20.7 and a variable linguistic personality is MEDIUM. Suggestions for further research can use other parameters in determining the level of personality of students with fuzzy logic

scholarly journals PEMANFAATAN FUZZY INFERENCE SYSTEM UNTUK MENENTUKAN DAMPAK COVID-19 TERHADAP PEREKONOMIAN DI KOTA BATAM

JURTEKSI ◽  
2021 ◽  
Vol 7 (3) ◽  
pp. 341-348
Author(s):  
Nanda Jarti
Keyword(s):  
Decision Making ◽  
Common Cold ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Human Life ◽  
Inference System ◽  
Corona Virus ◽  
Input Variables ◽  
The Impact ◽  
The City

Abstract : Corona virus is a very dangerous virus and can kill human life. This virus causes minor illnesses and serious illnesses such as colds or colds, since the emergence of the Corona Virus or Covid 19 paralyzing all human activities carried out outside the home. The problem of this research is in the form of the impact of the corona virus on the economy, especially in the city of Batam so that the residents of Batam can overcome this corona virus outbreak to improve the weakening economy. The main objective of this research is to examine the impact of Covid 19 on the economy of the Batam population so that the Batam population can improve the already weakened economy. This study uses Fuzzy Inference Sistym the Mamdani Method for Decision Making, using Operators or through the process of Fuzification of Input Variables, Inference Machines to process rules and produce Defuzification to get the final value  Keywords: corona prediction fuzzy inference system; mamdani method  Abstrak:Virus Corona merupakan  sebuah virus yang sangat berbahaya dan  bisa menghilangkan nyawa manusia. Virus ini  mengakibatkan penyakit  ringan dan penyakit berat  seperti common cold atau pilek, Sejak munculnya Virus Corona  atau Covid 19 melumpuhkan semua  kegiatan aktivitas manusia  yang dilakukan diluar rumah. Permasalahan  Penelitian ini berupa dampak akibat virus corona terhadap perekonomian khususnya pada Kota Batam sehingga penduduk Batam bisa mengatasi Wabah Virus corona ini untuk meningkatkan perekonomian yang semakin melemah. Tujuan Utama Penelitian ini mengkaji Dampak akibat Covid 19 terhadap perekonomian penduduk batam sehingga  penduduk Batam bisa meningkatkan perekonomian yang sudah melemah. Penelitian ini menggunakan Fuzzy Inference Sistem  Metode Mamdani untuk Pengambilan sistem Keputusan, menggunakan Operator Or dan melalui proses Fuzifikasi penentuan Variabel Input, Mesin Inferensi untuk melakukan proses aturan dan menghasilkan Defuzifikasi untuk mendapatkan nilai akhir. Kata Kunci : fuzzy inference sistem;  metode mamdani; prediksi corona

scholarly journals Fuzzy Logic for Intelligent Control System Using Soft Computing Applications

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2617
Author(s):  
Catalin Dumitrescu ◽  
Petrica Ciotirnae ◽  
Constantin Vizitiu
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Soft Computing ◽  
Intelligent Control ◽  
Fuzzy Controller ◽  
Control Technique ◽  
Distributed Intelligence ◽  
High Level ◽  
The Impact ◽  
Fuzzy Subsets

When considering the concept of distributed intelligent control, three types of components can be defined: (i) fuzzy sensors which provide a representation of measurements as fuzzy subsets, (ii) fuzzy actuators which can operate in the real world based on the fuzzy subsets they receive, and, (iii) the fuzzy components of the inference. As a result, these elements generate new fuzzy subsets from the fuzzy elements that were previously used. The purpose of this article is to define the elements of an interoperable technology Fuzzy Applied Cell Control-soft computing language for the development of fuzzy components with distributed intelligence implemented on the DSP target. The cells in the network are configured using the operations of symbolic fusion, symbolic inference and fuzzy–real symbolic transformation, which are based on the concepts of fuzzy meaning and fuzzy description. The two applications presented in the article, Agent-based modeling and fuzzy logic for simulating pedestrian crowds in panic decision-making situations and Fuzzy controller for mobile robot, are both timely. The increasing occurrence of panic moments during mass events prompted the investigation of the impact of panic on crowd dynamics and the simulation of pedestrian flows in panic situations. Based on the research presented in the article, we propose a Fuzzy controller-based system for determining pedestrian flows and calculating the shortest evacuation distance in panic situations. Fuzzy logic, one of the representation techniques in artificial intelligence, is a well-known method in soft computing that allows the treatment of strong constraints caused by the inaccuracy of the data obtained from the robot’s sensors. Based on this motivation, the second application proposed in the article creates an intelligent control technique based on Fuzzy Logic Control (FLC), a feature of intelligent control systems that can be used as an alternative to traditional control techniques for mobile robots. This method allows you to simulate the experience of a human expert. The benefits of using a network of fuzzy components are not limited to those provided distributed systems. Fuzzy cells are simple to configure while also providing high-level functions such as mergers and decision-making processes.

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