International Journal of Intelligent Systems and Applications

О журнале:

International Journal of Intelligent Systems and Applications (IJISA) is a peer reviewed journal in the field of Intelligent Systems and Applications. The journal is published 12 issues per year by the MECS Publisher from 2012. All papers will be blind reviewed. Accepted papers will be available on line (free access) and in printed version. No publication fee.

IJISA is publishing refereed, high quality original research papers in all areas of Intelligent Systems and Applications.

IJISA has been indexed by several world class databases: Scopus, Google Scholar, Microsoft Academic Search, CrossRef, DOAJ, IndexCopernicus, INSPEC(IET), EBSCO, JournalSeek, ULRICH's Periodicals Directory, WordCat, Scirus, Academic Journals Database, Stanford University Libraries, Cornell University Library, UniSA Library, CNKI Scholar, ProQuest, J-Gate, ZDB, BASE, OhioLINK, iThenticate, Open Access Articles, Open Science Directory, National Science Library of Chinese Academy of Sciences, The HKU Scholars Hub, etc...

The journal publishes original papers in the field of Intelligent Systems and Applications which covers, but not limited to the following scope:

Neural Networks

Evolutionary Computing and Genetic Algorithms

Fuzzy Systems and Soft Computing

Ant Colony Optimization

Particle Swarm Optimization

Artificial Fish School Algorithm

Artificial Life and Artificial Immune Systems

Systems Biology and Neurobiology

Support Vector Machine

Rough and fuzzy rough set

Knowledge Discovery and Data Mining

Kernel Methods

Supervised & Semi-supervised Learning

Cloud Computing

Evolutionary learning systems

Hybrid System

Man-Machine Interaction

CIMS and Manufacturing Systems

Factory Modeling and Simulation

Instrumentation Systems

Network-based Systems

Scheduling and Coordination

Process Automation

Automobile Electric

Sensor Fusion

Intelligent Mechatronics and Robotics

Intelligent Automation

Knowledge Management and Knowledge Engineering

Management Information Systems

Management of Supply Chain and Logistics

Financial Data Mining

Customer Relationship Management

Web Data Mining

Games Theory

System Theory and Control Theory

Nonlinear System and Control

Bayesian Network

Pervasive Computing

Modeling, Identification and Signal Processing

Fuzzy System and Fuzzy Control

Distributed Control Systems

Adaptive Control and Learning Control

Robust and H-infinity Control

Traffic Control

Communication Network Systems

Intelligence System Design

Учредители:

Modern Education & Computer Science Press

IDR (ID Readera):
journal-1501003
ISSN:
Печатный 2074-904X. Электронный 2074-9058.

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Статьи журнала

Fuzzy Clustering Data Given on the Ordinal Scale Based on Membership and Likelihood Functions Sharing

Fuzzy Clustering Data Given on the Ordinal Scale Based on Membership and Likelihood Functions Sharing

Zhengbing Hu, Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko, Viktoriia O. Samitova

Статья научная

A task of clustering data given on the ordinal scale under conditions of overlapping clusters has been considered. It's proposed to use an approach based on membership and likelihood functions sharing. A number of performed experiments proved effectiveness of the proposed method. The proposed method is characterized by robustness to outliers due to a way of ordering values while constructing membership functions.

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Fusion-Based Sensor Selection for Optimal State Estimation and Minimum Cost (Intelligent Optimization Approach)

Fusion-Based Sensor Selection for Optimal State Estimation and Minimum Cost (Intelligent Optimization Approach)

Saeed Mohammadloo, Ali Jabar Rashidi

Статья научная

This paper proposes a heuristic method for the sensor selection problem that uses a state vector fusion approach as a data fusion method. We explain the heuristic to estimate a stationary target position. Given a first sensor with specified accuracy and by using genetic algorithm, the heuristic selects second sensor such that the fusion of two sensor measurements would yield an optimal estimation in a target localization scenario. Optimality in our method means that a trade-off between estimation error and cost of sensory system should be created. The heuristic also investigates the importance of proportion between the range and bearing measurement accuracy of selected sensor. Monte Carlo Simulation results for a target position estimation scenario showed that the error in heuristic is less than the estimate error where sensors are used alone for estimation, while considering the trade-off between cost and accuracy.

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Parameter Tuning via Genetic Algorithm of Fuzzy Controller for Fire Tube Boiler

Parameter Tuning via Genetic Algorithm of Fuzzy Controller for Fire Tube Boiler

Osama I. Hassanein, Ayman A. Aly, Ahmed A. Abo-Ismail

Статья научная

The optimal use of fuel energy and water in a fire tube boiler is important in achieving economical system operation, precise control system design required to achieve high speed of response with no overshot. Two artificial intelligence techniques, fuzzy control (FLC) and genetic-fuzzy control (GFLC) applied to control both of the water/steam temperature and water level control loops of boiler. The parameters of the FLC are optimized to locating the optimal solutions to meet the required performance objectives using a genetic algorithm. The parameters subject to optimization are the width of the membership functions and scaling factors. The performance of the fire tube boiler that fitted with GFLC has reliable dynamic performance as compared with the system fitted with FLC.

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New Condition of Stabilization of Uncertain Continuous Takagi-Sugeno Fuzzy System based on Fuzzy Lyapunov Function

New Condition of Stabilization of Uncertain Continuous Takagi-Sugeno Fuzzy System based on Fuzzy Lyapunov Function

Yassine Manai, Mohamed Benrejeb

Статья научная

This paper deals with the stabilization of Takagi-Sugeno fuzzy models. Using non-quadratic Lyapunov function, new sufficient stabilization criteria with PDC controller are established in terms of Linear Matrix Inequality. Finally, a stabilization condition for uncertain system is given.

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