scholarly journals Formulizing the Fuzzy Rule for Takagi-Sugeno Model in Network Traffic Control

2018 ◽  
Vol 12 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Mohammad Alhihi ◽  
Mohammad Reza Khosravi
Keyword(s):  
Fuzzy Logic ◽  
Communication Networks ◽  
Traffic Control ◽  
Engineering Application ◽  
Fuzzy Rule ◽  
Proposed Model ◽  
Simulation Based ◽  
Fuzzy Neural ◽  
Fuzzy Logic Theory ◽  
Takagi Sugeno

Background:Nowadays, fuzzy logic theory is a popular approach to control network variables in engineering problems such as computer and communication networking. In this research, we formulize a new fuzzy logic-based rule for an important engineering application,i.e., traffic control of communication networks.Method:In this regard, we propose a new formulization based on a well-known model of traffic control in the networks entitled Takagi-Sugeno. Towards this modeling, we use a typical Fuzzy Neural Network (FNN) with an optimizer based on Genetic Algorithm (GA).Conclusion:The simulation results of our new model clearly prove that the proposed model and its formulation are approximately according to a theoretically consumed model for the problem. In details, we suppose two arbitrary examples for the problem which have two different assumed solutions, and then, we try to resolve the problem for both conditions based on the model in which the simulations show relatively similar results for both simulation-based and theoretical results in both examples.

Integration of fuzzy logic in the SEIRD compartmental model for the analysis of intervention and transmission heterogeneity on SARS-CoV-2 transmission dynamics

2022 ◽  
Author(s):  
Zakaria Shams Siam ◽  
Rubyat Tasnuva Hasan ◽  
Hossain Ahamed ◽  
Samiya Kabir Youme ◽  
Soumik Sarker Anik ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Compartmental Model ◽  
Fuzzy Rule ◽  
Transmission Dynamics ◽  
Intervention Measures ◽  
Proposed Model ◽  
Total Death ◽  
Linguistic Intervention ◽  
Fuzzy Linguistic ◽  
Death Cases

Different epidemiological compartmental models have been presented to predict the transmission dynamics of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this study, we have proposed a fuzzy rule-based Susceptible-Exposed-Infectious-Recovered-Death ([Formula: see text]) compartmental model considering a new dynamic transmission possibility variable as a function of time and three different fuzzy linguistic intervention variables to delineate the intervention and transmission heterogeneity on SARS-CoV-2 viral infection. We have analyzed the datasets of active cases and total death cases of China and Bangladesh. Using our model, we have predicted active cases and total death cases for China and Bangladesh. We further presented the correspondence of different intervention measures in relaxing the transmission possibility. The proposed model delineates the correspondence between the intervention measures as fuzzy subsets and the predicted active cases and total death cases. The prediction made by our system fitted the collected dataset very well while considering different fuzzy intervention measures. The integration of fuzzy logic in the classical compartmental model also produces more realistic results as it generates a dynamic transmission possibility variable. The proposed model could be used to control the transmission of SARS-CoV-2 as it deals with the intervention and transmission heterogeneity on SARS-CoV-2 transmission dynamics.

scholarly journals Study on Sentiment Classification Strategies Based on the Fuzzy Logic with Crow Search Algorithm

2022 ◽  
Author(s):  
Mazen Mohammed ◽  
Lasheng Yu ◽  
Ali Aldhubri ◽  
Gamil R. S.Qaid
Keyword(s):  
Fuzzy Logic ◽  
Search Algorithm ◽  
Fuzzy Rule ◽  
Machine Learning Algorithms ◽  
Sentiment Classification ◽  
Data Sets ◽  
Fuzzy Logic Algorithm ◽  
Rule Based System ◽  
Proposed Model ◽  
Media Applications

Abstract In recent times, sentiment analysis research has gained wide popularity. That situation is caused by the nature of online applications that allow users to express their opinions on events, services, or products through social media applications such as Twitter, Facebook, and Amazon. This paper proposes a novel sentiment classification method according to the Fuzzy rule-based system (FRBS) with crow search algorithm (CSA). FRBS is used to classify the polarity of sentences or documents, and the CSA is employed to optimize the best output from the fuzzy logic algorithm. The FRBS is applied to extract the sentiment and classify its polarity into negative, neutral, and positive. Sometimes, the outputs of the FRBS must be enhanced, especially since many variables are present and the rules between them overlap. For such cases, the CSA is used to solve this limitation faced by FRBS to optimize the outputs of FRBS and achieve the best result. We compared the performance of our proposed model with different machine learning algorithms, such as SVM, maximum entropy, boosting, and SWESA. We tested our model on three famous data sets collected from Amazon, Yelp, and IMDB. Experimental results demonstrated the effectiveness of the proposed model and achieved competitive performance in terms of accuracy, recall, precision, and the F–score.

scholarly journals Fuzzy-based quantification of congestion for traffic control

2021 ◽  
Vol 72 (1) ◽  
pp. 1-8
Author(s):  
Dinh Toan Trinh
Keyword(s):  
Fuzzy Logic ◽  
Traffic Flow ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Fuzzy Rule ◽  
Gradual Transition ◽  
Rule Base ◽  
State Variables ◽  
Study Results ◽  
Traffic Stream

This paper presents a methodology for appraisal of congestion level for traffic control on expressways using fuzzy logic. The congestion level indicates the severity of congestion and is estimated using speed and density, being the basic traffic parameters that describe state of a traffic stream. Formulation of the fuzzy rule base is made based on knowledge on traffic flow theory and engineering judgments. Field data on a segment of the Pan-Island Expressway of Singapore were used to estimate the congestion levels for three scenarios: single input variable (speed or density) and combined input variables (speed and density), represented by congestion level on a [0 1] scale. The results showed that there were big gaps between the congestion levels evaluated based specifically on speed and density alone (single state variable), and the congestion levels estimated from both variables lie in between. Given the uncertainty in traffic data collection and dynamic nature of traffic flow, this indicates that it may be inadequate to evaluate traffic congestion level using a single variable, and the use of both speed and density represent the state of a traffic stream more properly. The study results also show that the fuzzy logic approach provides flexible combination of state variables to obtain the congestion level and to describe gradual transition of traffic state, which is particularly important under the heavy congested conditions.

Multi-gene genetic programming to building up fuzzy rule-base in Neo-Fuzzy-Neuron networks

2021 ◽  
pp. 1-18
Author(s):  
Glender Brás ◽  
Alisson Marques Silva ◽  
Elizabeth Fialho Wanner
Keyword(s):  
Genetic Programming ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Genetic Operators ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Gradient Based ◽  
Non Linear System ◽  
Takagi Sugeno ◽  
Fuzzy Network

This paper introduces a new approach to build the rule-base on Neo-Fuzzy-Neuron (NFN) Networks. The NFN is a Neuro-Fuzzy network composed by a set of n decoupled zero-order Takagi-Sugeno models, one for each input variable, each one containing m rules. Employing Multi-Gene Genetic Programming (MG-GP) to create and adjust Gaussian membership functions and a Gradient-based method to update the network parameters, the proposed model is dubbed NFN-MG-GP. In the proposed model, each individual of MG-GP represents a complete rule-base of NFN. The rule-base is adjusted by genetic operators (Crossover, Reproduction, Mutation), and the consequent parameters are updated by a predetermined number of Gradient method epochs, every generation. The algorithm uses Elitism to ensure that the best rule-base is not lost between generations. The performance of the NFN-MG-GP is evaluated using instances of time series forecasting and non-linear system identification problems. Computational experiments and comparisons against state-of-the-art alternative models show that the proposed algorithms are efficient and competitive. Furthermore, experimental results show that it is possible to obtain models with good accuracy applying Multi-Gene Genetic Programming to construct the rule-base on NFN Networks.

scholarly journals Sleep EEG Classification Using Fuzzy Logic

2015 ◽  
Vol 1 (1) ◽  
pp. 17 ◽  
Author(s):  
Chetna Nagpal ◽  
P.K. Uppadhyay
Keyword(s):  
Fuzzy Logic ◽  
Slow Wave Sleep ◽  
Detection System ◽  
Fuzzy Rule ◽  
Fuzzy Classification ◽  
Rule Base ◽  
Eeg Signals ◽  
Multi Stage ◽  
Proposed Model ◽  
Stage System

the computerized detection of multi stage system of EEG signals using fuzzy logic has been developed and tested on prerecorded data of the EEG of rats.The multistage detection system consists of three major stages: Awake, SWS (Slow wave sleep), REM (Rapid eye movement) which has been recorded and can be detected by the fuzzy classification and fuzzy rule base. The proposed work approaches to identify thestage of 3- channel signal on the basis of frequency distribution of EEG, standard deviation of EOG and EMG, variance of EOG and EMG. Based on feature extracted data, fuzzy logic rule base modelwas evaluated accurately in terms of 3 stages (Awake, SWS, and REM) and the result confirmed that the proposed model has potential in classifying the EEG signals

scholarly journals A Fuzzy Rule-Based System for Classification of Diabetes

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8095
Author(s):  
Khalid Mahmood Aamir ◽  
Laiba Sarfraz ◽  
Muhammad Ramzan ◽  
Muhammad Bilal ◽  
Jana Shafi ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Early Diagnosis ◽  
Early Stage ◽  
Fuzzy Rule ◽  
Healthcare Sector ◽  
Diagnostic Process ◽  
Rule Based ◽  
Rule Based System ◽  
Proposed Model ◽  
Early Diagnosis Of Diabetes

Diabetes is a fatal disease that currently has no treatment. However, early diagnosis of diabetes aids patients to start timely treatment and thus reduces or eliminates the risk of severe complications. The prevalence of diabetes has been rising rapidly worldwide. Several methods have been introduced to diagnose diabetes at an early stage, however, most of these methods lack interpretability, due to which the diagnostic process cannot be explained. In this paper, fuzzy logic has been employed to develop an interpretable model and to perform an early diagnosis of diabetes. Fuzzy logic has been combined with the cosine amplitude method, and two fuzzy classifiers have been constructed. Afterward, fuzzy rules have been designed based on these classifiers. Lastly, a publicly available diabetes dataset has been used to evaluate the performance of the proposed fuzzy rule-based model. The results show that the proposed model outperforms existing techniques by achieving an accuracy of 96.47%. The proposed model has demonstrated great prediction accuracy, suggesting that it can be utilized in the healthcare sector for the accurate diagnose of diabetes.

Modeling of Self-Induced Vibrations that Occur During the Machining Process of Casting Patterns with the Use of The Fuzzy-Neural Networks Method

2013 ◽  
Vol 58 (3) ◽  
pp. 871-875
Author(s):  
A. Herberg
Keyword(s):  
Neural Networks ◽  
Control System ◽  
Network Structure ◽  
Fuzzy Systems ◽  
Fuzzy Neural Networks ◽  
Cnc Machining ◽  
Machining Process ◽  
Modeling Process ◽  
Fuzzy Neural ◽  
Takagi Sugeno

Abstract This article outlines a methodology of modeling self-induced vibrations that occur in the course of machining of metal objects, i.e. when shaping casting patterns on CNC machining centers. The modeling process presented here is based on an algorithm that makes use of local model fuzzy-neural networks. The algorithm falls back on the advantages of fuzzy systems with Takagi-Sugeno-Kanga (TSK) consequences and neural networks with auxiliary modules that help optimize and shorten the time needed to identify the best possible network structure. The modeling of self-induced vibrations allows analyzing how the vibrations come into being. This in turn makes it possible to develop effective ways of eliminating these vibrations and, ultimately, designing a practical control system that would dispose of the vibrations altogether.

scholarly journals Adaptive Fuzzy Approximation Control of PV Grid-Connected Inverters

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 942
Author(s):  
Myada Shadoul ◽  
Hassan Yousef ◽  
Rashid Al Abri ◽  
Amer Al-Hinai
Keyword(s):  
Adaptive Fuzzy Control ◽  
System Stability ◽  
Adaptive Fuzzy ◽  
Fuzzy Approximation ◽  
Neural Network Controller ◽  
Network Controller ◽  
Fuzzy Neural ◽  
Modelling Uncertainties ◽  
Grid Connected Inverter ◽  
Takagi Sugeno

Three-phase inverters are widely used in grid-connected renewable energy systems. This paper presents a new control methodology for grid-connected inverters using an adaptive fuzzy control (AFC) technique. The implementation of the proposed controller does not need prior knowledge of the system mathematical model. The capabilities of the fuzzy system in approximating the nonlinear functions of the grid-connected inverter system are exploited to design the controller. The proposed controller is capable to achieve the control objectives in the presence of both parametric and modelling uncertainties. The control objectives are to regulate the grid power factor and the dc output voltage of the photovoltaic systems. The closed-loop system stability and the updating laws of the controller parameters are determined via Lyapunov analysis. The proposed controller is simulated under different system disturbances, parameters, and modelling uncertainties to validate the effectiveness of the designed controller. For evaluation, the proposed controller is compared with conventional proportional-integral (PI) controller and Takagi–Sugeno–Kang-type probabilistic fuzzy neural network controller (TSKPFNN). The results demonstrated that the proposed AFC showed better performance in terms of response and reduced fluctuations compared to conventional PI controllers and TSKPFNN controllers.

scholarly journals Adaptive speed control method for electromagnetic direct drive vehicle robot driver based on fuzzy logic

2019 ◽  
Vol 52 (9-10) ◽  
pp. 1344-1353 ◽  
Author(s):  
Gang Chen ◽  
Weigong Zhang ◽  
Xu Li ◽  
Bing Yu
Keyword(s):  
Fuzzy Logic ◽  
Control Method ◽  
Tracking Error ◽  
Speed Control ◽  
System Structure ◽  
Direct Drive ◽  
Test Standards ◽  
Speed Controller ◽  
Fuzzy Neural ◽  
Vehicle Test

To solve the shortcomings of existing control methods for an electromagnetic direct drive vehicle robot driver, including large speed tracking error and large mileage deviation, a new adaptive speed control method for the electromagnetic direct drive vehicle robot driver based on fuzzy logic is proposed in this paper. The electromagnetic direct drive vehicle robot driver adapts an electromagnetic linear motor as its drive mechanism. The control system structure is designed. The coordinated controller for multiple manipulators is presented. Moreover, an adaptive speed controller for the electromagnetic direct drive vehicle robot driver is proposed to achieve the accurate tracking of desired speed. Experiments are conducted using a Ford FOCUS car. Performances of the proposed method, proportional–integral–derivative, and fuzzy neural network are compared and analyzed. Experimental results demonstrate that the proposed control method can accurately track the target speed, and it can inhabit the change of speed caused by interference under different test conditions, and it has small mileage deviation, which can meet the requirements of national vehicle test standards.

Sign in / Sign up

Export Citation Format

Share Document