An Intelligent Fuzzy Logic System for Network Congestion Control

2017 ◽  
Vol 2 (11) ◽  
pp. 23-30 ◽  
Author(s):  
Uguta Henry Preye ◽  
Onyejegbu Laeticia Nneka
Keyword(s):  
Fuzzy Logic ◽  
Queue Length ◽  
Network Performance ◽  
Early Stage ◽  
Fuzzy Logic System ◽  
Queue Size ◽  
Network Congestion ◽  
Inference System ◽  
Dropping Probability ◽  
Logic System

Network congestion is a major problem in all network environments as such it calls for ways to manage this problem. In this paper, we propose a Fuzzy Regulator Effective Random Early Detection (FRERED) system, which is an intelligent fuzzy logic based controller technique for early stage congestion detection, at the router buffer in the networks. The proposed technique extends the Fuzzy-Based system in the Fuzzy Hybrid ERED algorithm by considering the delay variable in its inference system to ease the problem of parameter initialization and parameter dependency. Unlike the Fuzzy-Based controller in the existing Fuzzy Hybrid ERED system which uses two parameter settings in its inference system that is, the queue size and average queue length in computing the dropping probability of packets. The proposed technique uses the queue size, average queue length and the delay approximation as input variables in computing the packet drop probability. The applied fuzzy logic system yields an output that denotes a packet dropping probability, which in turn controls and prevents congestion in early stage. This was achieved after simulating the proposed technique and the existing Fuzzy-Based controller using Matlab. The results obtained shows that this approach results in less packet drops for about the same link utilization as the existing Fuzzy-Based controller. Therefore, this technique, generally, controls network congestion and improves network performance. The methodology used to achieve this is the object oriented methodology and JAVA programming language was used to develop the system.

scholarly journals The Method of Evaluation Degree of Changes Confidentiality Attribute of Information Asset Inside ICT System / Metoda Oceny Stopnia Zmian Atrybutu Poufności Zasobu Informacyjnego w Systemie Teleinformatycznym

2015 ◽  
Vol 33 (1) ◽  
pp. 159-168
Author(s):  
Artur Szleszyński
Keyword(s):  
Fuzzy Logic ◽  
Value Of Information ◽  
Fuzzy Logic System ◽  
Inference System ◽  
Fuzzy Variables ◽  
Ict System ◽  
Information Confidentiality ◽  
Method Of Evaluation ◽  
Logic System ◽  
The Way

Abstract The work presents the proposition of inference system that evaluates the possibility of information confidentiality attribute violation. Crisp and fuzzy variables have been defined. Values of output variables from fuzzy logic system have been presented. The way of counting the modified value of information confidentiality attribute has been described.

Navigational path analysis of mobile robots using an adaptive neuro-fuzzy inference system controller in a dynamic environment

2010 ◽  
Vol 224 (6) ◽  
pp. 1369-1381 ◽  
Author(s):  
D R Parhi ◽  
M K Singh
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Mobile Robots ◽  
Fuzzy Inference ◽  
Dynamic Environment ◽  
Fuzzy Logic System ◽  
Inference System ◽  
The Neural Network ◽  
Neuro Fuzzy ◽  
Logic System

This article focuses on the navigational path analysis of mobile robots using the adaptive neuro-fuzzy inference system (ANFIS) in a cluttered dynamic environment. In the ANFIS controller, after the input layer there is a fuzzy layer and the rest of the layers are neural network layers. The adaptive neuro-fuzzy hybrid system combines the advantages of the fuzzy logic system, which deals with explicit knowledge that can be explained and understood, and those of the neural network, which deals with implicit knowledge that can be acquired by learning. The inputs to the fuzzy logic layer include the front obstacle distance, the left obstacle distance, the right obstacle distance, and target steering. A learning algorithm based on the neural network technique has been developed to tune the parameters of fuzzy membership functions, which smooth the trajectory generated by the fuzzy logic system. Using the developed ANFIS controller, the mobile robots are able to avoid static and dynamic obstacles and reach the target successfully in cluttered environments. The experimental results agree well with the simulation results; this proves the authenticity of the theory developed.

scholarly journals Behaviour based Mobile Robot Navigation Technique using AI System: Experimental Investigation on Active Media Pioneer Robot

1970 ◽  
Vol 6 (2) ◽  
Author(s):  
S. Parasuraman, V.Ganapathy ◽  
Bijan Shirinzadeh
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Robot Navigation ◽  
Fuzzy Logic System ◽  
Mobile Robot Navigation ◽  
Inference System ◽  
Human Expert ◽  
Active Media ◽  
Alpha Level ◽  
Logic System

A key issue in the research of an autonomous robot is the design and development of the navigation technique that enables the robot to navigate in a real world environment. In this research, the issues investigated and methodologies established include (a) Designing of the individual behavior and behavior rule selection using Alpha level fuzzy logic system  (b) Designing of the controller, which maps the sensors input to the motor output through model based Fuzzy Logic Inference System and (c) Formulation of the decision-making process by using Alpha-level fuzzy logic system. The proposed method is applied to Active Media Pioneer Robot and the results are discussed and compared with most accepted methods. This approach provides a formal methodology for representing and implementing the human expert heuristic knowledge and perception-based action in mobile robot navigation. In this approach, the operational strategies of the human expert driver are transferred via fuzzy logic to the robot navigation in the form of a set of simple conditional statements composed of linguistic variables.Keywards: Mobile robot, behavior based control, fuzzy logic, alpha level fuzzy logic, obstacle avoidance behavior and goal seek behavior

scholarly journals A Hybrid Computational Method of Desirability, Fuzzy Logic, ANFIS, and LAPO Algorithm for Multiobjective Optimization Design of Scott Russell Compliant Mechanism

2020 ◽  
Vol 2020 ◽  
pp. 1-28 ◽  
Author(s):  
Ngoc Thoai Tran ◽  
Ngoc Le Chau ◽  
Thanh-Phong Dao
Keyword(s):  
Fuzzy Logic ◽  
Multiobjective Optimization ◽  
Optimization Design ◽  
Compliant Mechanisms ◽  
Compliant Mechanism ◽  
Fuzzy Logic System ◽  
Computational Method ◽  
Inference System ◽  
Design Variables ◽  
Logic System

Flexure-based compliant mechanisms are increasingly promising in precision engineering, robotics, and other applications, thanks to the excellent advantages of no friction, no backlash, no wear, and minimal assembly. However, their design and analysis are still challenging due to the coupling of kinematic-mechanical behaviors with large nonlinear deflections in comparison to their rigid-body counterparts. Optimal design is an important aspect in the field of compliant mechanisms and has attracted much attention during the last decades. Especially, when considering a multiobjective optimization design for compliant mechanisms, the problem is becoming more complicated. Thus, this article presents a new efficient hybrid computational method to resolve multiobjective optimization design of compliant mechanisms. A Scott Russell compliant mechanism is employed as the design example and to show the advantages of the proposed optimizing method. The proposed method is developed by hybridizing the desirability function approach, fuzzy logic system, adaptive neuro-fuzzy inference system (ANFIS), and lightning attachment procedure optimization (LAPO). First of all, a 3D finite element model is created and central composite design is employed to build a numerical matrix. First, design variables are refined by using analysis of variance and Taguchi approach in terms of considerably eliminating space of design variables. Subsequently, desirability values of two objective functions are determined and transferred into the fuzzy logic system. The output of the fuzzy logic system is considered as a single combined objective function. Next, modeling for fuzzy output is established via developing the ANFIS model. At last, the LAPO algorithm is adopted for solving the multiobjective optimization problem for the mechanism. Three numerical examples are investigated to validate the feasibility and the effectiveness of performance efficiency of the proposed methodology. The results find that the proposed methodology is more efficient than traditional Taguchi-based fuzzy logic. Besides, the performance efficiency of the proposed approach outperforms the Jaya algorithm and TLBO algorithm through the Wilcoxon signed rank test and Friedman test. The results of this article give a useful approach for complex optimization problems.

A New Hardware Architecture for Fuzzy Logic System Acceleration

2016 ◽  
Vol 12 (2) ◽  
pp. 188-197
Author(s):  
A yahoo.com ◽  
Aumalhuda Gani Abood aumalhuda ◽  
A comp ◽  
Dr. Mohammed A. Jodha ◽  
Dr. Majid A. Alwan
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic System ◽  
Hardware Architecture ◽  
Logic System

Penerapan Logika Samar dalam Peramalan Data Runtun Waktu

2011 ◽  
Vol 3 (2) ◽  
pp. 11-15
Author(s):  
Seng Hansun
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Time Series Analysis ◽  
Fuzzy Sets ◽  
Fuzzy System ◽  
Time Series Data ◽  
Fuzzy Logic System ◽  
Series Data ◽  
Series Analysis ◽  
Logic System

Recently, there are so many soft computing methods been used in time series analysis. One of these methods is fuzzy logic system. In this paper, we will try to implement fuzzy logic system to predict a non-stationary time series data. The data we use here is Mackey-Glass chaotic time series. We also use MATLAB software to predict the time series data, which have been divided into four groups of input-output pairs. These groups then will be used as the input variables of the fuzzy logic system. There are two scenarios been used in this paper, first is by using seven fuzzy sets, and second is by using fifteen fuzzy sets. The result shows that the fuzzy system with fifteen fuzzy sets give a better forecasting result than the fuzzy system with seven fuzzy sets. Index Terms—forecasting, fuzzy logic, Mackey-Glass chaotic, MATLAB, time series analysis

IMPROVING EIGENSPACE-BASED FUZZY LOGIC SYSTEM USING A LINEAR INTERPOLATION SCHEME FOR SPEECH PATTERN RECOGNITION

2013 ◽  
Vol 37 (3) ◽  
pp. 611-620
Author(s):  
Ing-Jr Ding ◽  
Chih-Ta Yen
Keyword(s):  
Pattern Recognition ◽  
Fuzzy Logic ◽  
Recognition Performance ◽  
Linear Interpolation ◽  
Fuzzy Logic System ◽  
Interpolation Scheme ◽  
Speech Pattern ◽  
Eigen Value ◽  
Target Speaker ◽  
Logic System

The Eigen-FLS approach using an eigenspace-based scheme for fast fuzzy logic system (FLS) establishments has been attempted successfully in speech pattern recognition. However, speech pattern recognition by Eigen-FLS will still encounter a dissatisfactory recognition performance when the collected data for eigen value calculations of the FLS eigenspace is scarce. To tackle this issue, this paper proposes two improved-versioned Eigen-FLS methods, incremental MLED Eigen-FLS and EigenMLLR-like Eigen-FLS, both of which use a linear interpolation scheme for properly adjusting the target speaker’s Eigen-FLS model derived from an FLS eigenspace. Developed incremental MLED Eigen-FLS and EigenMLLR-like Eigen-FLS are superior to conventional Eigen-FLS especially in the situation of insufficient data from the target speaker.

Sign in / Sign up

Export Citation Format

Share Document