Search results for: the theory of fuzzy sets.

Authors: Mujde Turkkan, Nurkan Yagiz

Abstract:

In this study an active controller is presented for vibration suppression of a full-bus model. The bus is modeled having seven degrees of freedom. Using the achieved model via Lagrange Equations the system equations of motion are derived. The suspensions of the bus model include air springs with two auxiliary chambers are used. Fuzzy logic controller is used to improve the ride comfort. The numerical results, verifies that the presented fuzzy logic controller improves the ride comfort.

Keywords: Ride comfort, air spring, bus, fuzzy logic controller.

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Authors: Hsuan-Ku Liu

Abstract:

A linear system is called a fully fuzzy linear system (FFLS) if quantities in this system are all fuzzy numbers. For the FFLS, we investigate its solution and develop a new approximate method for solving the FFLS. Observing the numerical results, we find that our method is accurate than the iterative Jacobi and Gauss- Seidel methods on approximating the solution of FFLS.

Keywords: Fully fuzzy linear equations, iterative method, homotopy perturbation method, approximate solutions.

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Authors: Takashi Mitsuishi, Kiyoshi Sawada, Yasunari Shidama

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The mathematical framework for studying of a fuzzy approximate reasoning is presented in this paper. Two important defuzzification methods (Area defuzzification and Height defuzzification) besides the center of gravity method which is the best well known defuzzification method are described. The continuity of the defuzzification methods and its application to a fuzzy feedback control are discussed.

Keywords: Fuzzy approximate reasoning, defuzzification, area method, height method.

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Authors: Mohammad Najafi Nobar, Bahareh Pourmehr, Mehdi Hajimirarab

Abstract:

One of the main processes of supply chain management is supplier selection process which its accurate implementation can dramatically increase company competitiveness. In presented article model developed based on the features of second tiers suppliers and four scenarios are predicted in order to help the decision maker (DM) in making up his/her mind. In addition two tiers of suppliers have been considered as a chain of suppliers. Then the proposed approach is solved by a method combined of concepts of fuzzy set theory (FST) and linear programming (LP) which has been nourished by real data extracted from an engineering design and supplying parts company. At the end results reveal the high importance of considering second tier suppliers features as criteria for selecting the best supplier.

Keywords: Supply Chain Management (SCM), SupplierSelection, Second Tier Supplier, Scenario Planning, Green Factor, Linear Programming, Fuzzy Set Theory

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Authors: Vahid Rostami, Omid sojodishijani , Saeed Ebrahimijam, Ali MohsenizanjaniNejad

Abstract:

This paper is described one of the intelligent control method in Autonomous systems, which is called fuzzy control to correct the three wheel omnidirectional robot movement while it make mistake to catch the target. Fuzzy logic is especially advantageous for problems that can not be easily represented by mathematical modeling because data is either unavailable, incomplete or the process is too complex. Such systems can be easily up grated by adding new rules to improve performance or add new features. In many cases , fuzzy control can be used to improve existing traditional controller systems by adding an extra layer of intelligence to the current control method. The fuzzy controller designed here is more accurate and flexible than the traditional controllers. The project is done at MRL middle size soccer robot team.

Keywords: Robocup , omnidirectional , fuzzy control, soccer robot , intelligent control.

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Authors: Ahmad T. Al-Taani, Noor Aldeen K. Al-Awad

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In this study, a fuzzy similarity approach for Arabic web pages classification is presented. The approach uses a fuzzy term-category relation by manipulating membership degree for the training data and the degree value for a test web page. Six measures are used and compared in this study. These measures include: Einstein, Algebraic, Hamacher, MinMax, Special case fuzzy and Bounded Difference approaches. These measures are applied and compared using 50 different Arabic web-pages. Einstein measure was gave best performance among the other measures. An analysis of these measures and concluding remarks are drawn in this study.

Keywords: Text classification, HTML, web pages, machine learning, fuzzy logic, Arabic web pages.

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Authors: K. M. Alsager, N. O. Alshehri

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In this paper, we proposed the notion of single valued neutrosophic hesitant fuzzy rough set, by combining single valued neutrosophic hesitant fuzzy set and rough set. The combination of single valued neutrosophic hesitant fuzzy set and rough set is a powerful tool for dealing with uncertainty, granularity and incompleteness of knowledge in information systems. We presented both definition and some basic properties of the proposed model. Finally, we gave a general approach which is applied to a decision making problem in disease diagnoses, and demonstrated the effectiveness of the approach by a numerical example.

Keywords: Single valued neutrosophic hesitant set, single valued neutrosophic hesitant relation, single valued neutrosophic hesitant fuzzy rough set, decision making method.

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Authors: F.Alilat, S.Loumi, H.Merrad, B.Sansal

Abstract:

In this article a modification of the algorithm of the fuzzy ART network, aiming at returning it supervised is carried out. It consists of the search for the comparison, training and vigilance parameters giving the minimum quadratic distances between the output of the training base and those obtained by the network. The same process is applied for the determination of the parameters of the fuzzy ARTMAP giving the most powerful network. The modification consist in making learn the fuzzy ARTMAP a base of examples not only once as it is of use, but as many time as its architecture is in evolution or than the objective error is not reached . In this way, we don-t worry about the values to impose on the eight (08) parameters of the network. To evaluate each one of these three networks modified, a comparison of their performances is carried out. As application we carried out a classification of the image of Algiers-s bay taken by SPOT XS. We use as criterion of evaluation the training duration, the mean square error (MSE) in step control and the rate of good classification per class. The results of this study presented as curves, tables and images show that modified fuzzy ARTMAP presents the best compromise quality/computing time.

Keywords: Neural Networks, fuzzy ART, fuzzy ARTMAP, Remote sensing, multispectral Classification.

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Authors: Seo Young Kim, Tai Myong Choi

Abstract:

The main goal of microarray experiments is to quantify the expression of every object on a slide as precisely as possible, with a further goal of clustering the objects. Recently, many studies have discussed clustering issues involving similar patterns of gene expression. This paper presents an application of fuzzy-type methods for clustering DNA microarray data that can be applied to typical comparisons. Clustering and analyses were performed on microarray and simulated data. The results show that fuzzy-possibility c-means clustering substantially improves the findings obtained by others.

Keywords: Clustering, microarray data, Fuzzy-type clustering, Validation

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Authors: Ε. Giovanis

Abstract:

In this paper we present a Adaptive Neuro-Fuzzy System (ANFIS) with inputs the lagged dependent variable for the prediction of Gross domestic Product growth rate in six countries. We compare the results with those of Autoregressive (AR) model. We conclude that the forecasting performance of neuro-fuzzy-system in the out-of-sample period is much more superior and can be a very useful alternative tool used by the national statistical services and the banking and finance industry.

Keywords: Autoregressive model, Forecasting, Gross DomesticProduct, Neuro-Fuzzy

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Authors: Om Prakash Verma, Rajesh Singla, Rajesh Kumar

Abstract:

Conventional controller’s usually required a prior knowledge of mathematical modelling of the process. The inaccuracy of mathematical modelling degrades the performance of the process, especially for non-linear and complex control problem. The process used is Water-Bath system, which is most widely used and nonlinear to some extent. For Water-Bath system, it is necessary to attain desired temperature within a specified period of time to avoid the overshoot and absolute error, with better temperature tracking capability, else the process is disturbed.

To overcome above difficulties intelligent controllers, Fuzzy Logic (FL) and Adaptive Neuro-Fuzzy Inference System (ANFIS), are proposed in this paper. The Fuzzy controller is designed to work with knowledge in the form of linguistic control rules. But the translation of these linguistic rules into the framework of fuzzy set theory depends on the choice of certain parameters, for which no formal method is known. To design ANFIS, Fuzzy-Inference-System is combined with learning capability of Neural-Network.

It is analyzed that ANFIS is best suitable for adaptive temperature control of above system. As compared to PID and FLC, ANFIS produces a stable control signal. It has much better temperature tracking capability with almost zero overshoot and minimum absolute error.

Keywords: PID Controller, FLC, ANFIS, Non-Linear Control System, Water-Bath System, MATLAB-7.

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Authors: Mohamed Amine Haraoubia, Abdelaziz Hamzaoui, Najib Essounbouli

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The pursuit of the MPPT has led to the development of many kinds of controllers, one of which is the Fuzzy Logic controller, which has proven its worth. To further tune this controller this paper will discuss and analyze the use of Genetic Algorithms to tune the Fuzzy Logic Controller. It will provide an introduction to both systems, and test their compatibility and performance.

Keywords: Fuzzy logic controller (FLC), fuzzy logic (FL), genetic algorithm (GA), maximum power point (MPP), maximum power point tracking (MPPT).

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Authors: G.Wiselin Jiji, L.Ganesan

Abstract:

Textures are replications, symmetries and combinations of various basic patterns, usually with some random variation one of the gray-level statistics. This article proposes a new approach to Segment texture images. The proposed approach proceeds in 2 stages. First, in this method, local texture information of a pixel is obtained by fuzzy texture unit and global texture information of an image is obtained by fuzzy texture spectrum. The purpose of this paper is to demonstrate the usefulness of fuzzy texture spectrum for texture Segmentation. The 2nd Stage of the method is devoted to a decision process, applying a global analysis followed by a fine segmentation, which is only focused on ambiguous points. The above Proposed approach was applied to brain image to identify the components of brain in turn, used to locate the brain tumor and its Growth rate.

Keywords: Fuzzy Texture Unit, Fuzzy Texture Spectrum, andPattern Recognition, segmentation.

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Authors: C. Ardil

Abstract:

This article presents a multiple criteria evaluation approach to uncertainty, vagueness, and imprecision analysis for ranking alternatives with fuzzy data for decision making using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The fighter aircraft evaluation and selection decision making problem is modeled in a fuzzy environment with triangular fuzzy numbers. The fuzzy decision information related to the fighter aircraft selection problem is taken into account in ordering the alternatives and selecting the best candidate. The basic fuzzy TOPSIS procedure steps transform fuzzy decision matrices into matrices of alternatives evaluated according to all decision criteria. A practical numerical example illustrates the proposed approach to the fighter aircraft selection problem.

Keywords: triangular fuzzy number (TFN), multiple criteria decision making analysis, decision making, aircraft selection, MCDMA, fuzzy TOPSIS

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Authors: Jafreezal Jaafar, Eric McKenzie

Abstract:

This paper presents a solution for the behavioural animation of autonomous virtual agent navigation in virtual environments. We focus on using Dempster-Shafer-s Theory of Evidence in developing visual sensor for virtual agent. The role of the visual sensor is to capture the information about the virtual environment or identifie which part of an obstacle can be seen from the position of the virtual agent. This information is require for vitual agent to coordinate navigation in virtual environment. The virual agent uses fuzzy controller as a navigation system and Fuzzy α - level for the action selection method. The result clearly demonstrates the path produced is reasonably smooth even though there is some sharp turn and also still not diverted too far from the potential shortest path. This had indicated the benefit of our method, where more reliable and accurate paths produced during navigation task.

Keywords: Agent, navigation, Dempster Shafer, fuzzy logic.

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Authors: Abduladheem A. Ali, Easa A. Abd

Abstract:

The robot is a repeated task plant. The control of such a plant under parameter variations and load disturbances is one of the important problems. The aim of this work is to design Geno-Fuzzy controller suitable for online applications to control single link rigid robot arm plant. The genetic-fuzzy online controller (indirect controller) has two genetic-fuzzy blocks, the first as controller, the second as identifier. The identification method is based on inverse identification technique. The proposed controller it tested in normal and load disturbance conditions.

Keywords: Fuzzy network, genetic algorithm, robot control, online genetic control, parameter identification.

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Authors: M.M. Zahedi, L. Torkzadeh

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In this note first we define the notions of intuitionistic fuzzy dual positive implicative hyper K-ideals of types 1,2,3,4 and intuitionistic fuzzy dual hyper K-ideals. Then we give some classifications about these notions according to the level subsets. Also by given some examples we show that these notions are not equivalent, however we prove some theorems which show that there are some relationships between these notions. Finally we define the notions of product and antiproduct of two fuzzy subsets and then give some theorems about the relationships between the intuitionistic fuzzy dual positive implicative hyper K-ideal of types 1,2,3,4 and their (anti-)products, in particular we give a main decomposition theorem.

Keywords: hyper K-algebra, intuitionistic fuzzy dual positive implicative hyper K-ideal.

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Authors: Mehtap Dursun, E. Ertugrul Karsak, Melis Almula Karadayi

Abstract:

The management of the health-care wastes is one of the most important problems in Istanbul, a city with more than 12 million inhabitants, as it is in most of the developing countries. Negligence in appropriate treatment and final disposal of the healthcare wastes can lead to adverse impacts to public health and to the environment. This paper employs a fuzzy multi-criteria group decision making approach, which is based on the principles of fusion of fuzzy information, 2-tuple linguistic representation model, and technique for order preference by similarity to ideal solution (TOPSIS), to evaluate health-care waste (HCW) treatment alternatives for Istanbul. The evaluation criteria are determined employing nominal group technique (NGT), which is a method of systematically developing a consensus of group opinion. The employed method is apt to manage information assessed using multigranularity linguistic information in a decision making problem with multiple information sources. The decision making framework employs ordered weighted averaging (OWA) operator that encompasses several operators as the aggregation operator since it can implement different aggregation rules by changing the order weights. The aggregation process is based on the unification of information by means of fuzzy sets on a basic linguistic term set (BLTS). Then, the unified information is transformed into linguistic 2-tuples in a way to rectify the problem of loss information of other fuzzy linguistic approaches.

Keywords: Group decision making, health care waste management, multi-criteria decision making, OWA, TOPSIS, 2-tuple linguistic representation

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Authors: Lina Yan, Shiheng Wang, Ke Wang

Abstract:

A preconditioned Jacobi (PJ) method is provided for solving fuzzy linear systems whose coefficient matrices are crisp Mmatrices and the right-hand side columns are arbitrary fuzzy number vectors. The iterative algorithm is given for the preconditioned Jacobi method. The convergence is analyzed with convergence theorems. Numerical examples are given to illustrate the procedure and show the effectiveness and efficiency of the method.

Keywords: preconditioning, M-matrix, Jacobi method, fuzzy linear system (FLS).

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Authors: Muhammad M. A. S. Mahmoud

Abstract:

Natural gas sweetening process is a controlled process that must be done at maximum efficiency and with the highest quality. In this work, due to complexity and non-linearity of the process, the H2S gas separation and the intelligent fuzzy controller, which is used to enhance the process, are simulated in MATLAB – Simulink. New design of fuzzy control for Gas Separator is discussed in this paper. The design is based on the utilization of linear state-estimation to generate the internal knowledge-base that stores input-output pairs. The obtained input/output pairs are then used to design a feedback fuzzy controller. The proposed closed-loop fuzzy control system maintains the system asymptotically-stability while it enhances the system time response to achieve better control of the concentration of the output gas from the tower. Simulation studies are carried out to illustrate the Gas Separator system performance.

Keywords: Gas separator, gas sweetening, intelligent controller, fuzzy control.

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Authors: Chien-Hua Wang, Wei-Hsuan Lee, Chin-Tzong Pang

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In data mining, the association rules are used to find for the associations between the different items of the transactions database. As the data collected and stored, rules of value can be found through association rules, which can be applied to help managers execute marketing strategies and establish sound market frameworks. This paper aims to use Fuzzy Frequent Pattern growth (FFP-growth) to derive from fuzzy association rules. At first, we apply fuzzy partition methods and decide a membership function of quantitative value for each transaction item. Next, we implement FFP-growth to deal with the process of data mining. In addition, in order to understand the impact of Apriori algorithm and FFP-growth algorithm on the execution time and the number of generated association rules, the experiment will be performed by using different sizes of databases and thresholds. Lastly, the experiment results show FFPgrowth algorithm is more efficient than other existing methods.

Keywords: Data mining, association rule, fuzzy frequent patterngrowth.

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Authors: Ermanu A. Hakim, Adi Soeprijanto, Mauridhi H.P

Abstract:

This paper presents PSS (Power system stabilizer) design based on optimal fuzzy PID (OFPID). OFPID based PSS design is considered for single-machine power systems. The main motivation for this design is to stabilize or to control low-frequency oscillation on power systems. Firstly, describing the linear PID control then to combine this PID control with fuzzy logic control mechanism. Finally, Fuzzy PID parameters (Kp. Kd, KI, Kupd, Kui) are tuned by Genetic Algorthm (GA) to reach optimal global stability. The effectiveness of the proposed PSS in increasing the damping of system electromechanical oscillation is demonstrated in a one-machine-infinite-bus system

Keywords: Fuzzy PID, Genetic Algorithm, power system stabilizer.

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Authors: Chih-Jer Lin, Chun-Ying Lee, Ying Liu, Chiang-Ho Cheng

Abstract:

The main objective of this article is to present the semi-active vibration control using an electro-rheological fluid embedded sandwich structure for a cantilever beam. ER fluid is a smart material, which cause the suspended particles polarize and connect each other to form chain. The stiffness and damping coefficients of the ER fluid can be changed in 10 micro seconds; therefore, ERF is suitable to become the material embedded in the tunable vibration absorber to become a smart absorber. For the ERF smart material embedded structure, the fuzzy control law depends on the experimental expert database and the proposed self-tuning strategy. The electric field is controlled by a CRIO embedded system to implement the real application. This study investigates the different performances using the Type-1 fuzzy and interval Type-2 fuzzy controllers. The Interval type-2 fuzzy control is used to improve the modeling uncertainties for this ERF embedded shock absorber. The self-tuning vibration controllers using Type-1 and Interval Type-2 fuzzy law are implemented to the shock absorber system. Based on the resulting performance, Internal Type-2 fuzzy is better than the traditional Type-1 fuzzy control for this vibration control system.

 

Keywords: Electro-Rheological Fluid, Semi-active vibration control, shock absorber, type 2 fuzzy control.

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Authors: Aqeel S. Jaber, Abu Zaharin Ahmad, Ahmed N. Abdalla

Abstract:

The artificial intelligent controller in power system plays as most important rule for many applications such as system operation and its control specially Load Frequency Controller (LFC). The main objective of LFC is to keep the frequency and tie-line power close to their decidable bounds in case of disturbance. In this paper, parallel fuzzy PI adaptive with conventional PD technique for Load Frequency Control system was proposed. PSO optimization method used to optimize both of scale fuzzy PI and tuning of PD. Two equal interconnected power system areas were used as a test system. Simulation results show the effectiveness of the proposed controller compared with different PID and classical fuzzy PI controllers in terms of speed response and damping frequency.

Keywords: Load frequency control, PSO, fuzzy control.

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Authors: Myung-Won Lee, Keun-Chang Kwak

Abstract:

This paper is concerned with knowledge representation and extraction of fuzzy if-then rules using Interval Type-2 Context-based Fuzzy C-Means clustering (IT2-CFCM) with the aid of fuzzy granulation. This proposed clustering algorithm is based on information granulation in the form of IT2 based Fuzzy C-Means (IT2-FCM) clustering and estimates the cluster centers by preserving the homogeneity between the clustered patterns from the IT2 contexts produced in the output space. Furthermore, we can obtain the automatic knowledge representation in the design of Radial Basis Function Networks (RBFN), Linguistic Model (LM), and Adaptive Neuro-Fuzzy Networks (ANFN) from the numerical input-output data pairs. We shall focus on a design of ANFN in this paper. The experimental results on an estimation problem of energy performance reveal that the proposed method showed a good knowledge representation and performance in comparison with the previous works.

Keywords: IT2-FCM, IT2-CFCM, context-based fuzzy clustering, adaptive neuro-fuzzy network, knowledge representation.

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Authors: Sukhveer Singh, Sandeep Singh

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A bi-objective fuzzy transportation problem with the objectives to minimize the total fuzzy cost and fuzzy time of transportation without according priorities to them is considered. To the best of our knowledge, there is no method in the literature to find efficient solutions of the bi-objective transportation problem under uncertainty. In this paper, a bi-objective transportation problem in an uncertain environment has been formulated. An algorithm has been proposed to find efficient solutions of the bi-objective transportation problem under uncertainty. The proposed algorithm avoids the degeneracy and gives the optimal solution faster than other existing algorithms for the given uncertain transportation problem.

Keywords: Transportation problem, efficient solution, ranking function, fuzzy transportation problem.

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Authors: A. Borji, M. Hamidi

Abstract:

A new method for color image segmentation using fuzzy logic is proposed in this paper. Our aim here is to automatically produce a fuzzy system for color classification and image segmentation with least number of rules and minimum error rate. Particle swarm optimization is a sub class of evolutionary algorithms that has been inspired from social behavior of fishes, bees, birds, etc, that live together in colonies. We use comprehensive learning particle swarm optimization (CLPSO) technique to find optimal fuzzy rules and membership functions because it discourages premature convergence. Here each particle of the swarm codes a set of fuzzy rules. During evolution, a population member tries to maximize a fitness criterion which is here high classification rate and small number of rules. Finally, particle with the highest fitness value is selected as the best set of fuzzy rules for image segmentation. Our results, using this method for soccer field image segmentation in Robocop contests shows 89% performance. Less computational load is needed when using this method compared with other methods like ANFIS, because it generates a smaller number of fuzzy rules. Large train dataset and its variety, makes the proposed method invariant to illumination noise

Keywords: Comprehensive learning Particle Swarmoptimization, fuzzy classification.

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Authors: Amarunnishad T.M., Govindan V.K., Abraham T. Mathew

Abstract:

An image compression method has been developed using fuzzy edge image utilizing the basic Block Truncation Coding (BTC) algorithm. The fuzzy edge image has been validated with classical edge detectors on the basis of the results of the well-known Canny edge detector prior to applying to the proposed method. The bit plane generated by the conventional BTC method is replaced with the fuzzy bit plane generated by the logical OR operation between the fuzzy edge image and the corresponding conventional BTC bit plane. The input image is encoded with the block mean and standard deviation and the fuzzy bit plane. The proposed method has been tested with test images of 8 bits/pixel and size 512×512 and found to be superior with better Peak Signal to Noise Ratio (PSNR) when compared to the conventional BTC, and adaptive bit plane selection BTC (ABTC) methods. The raggedness and jagged appearance, and the ringing artifacts at sharp edges are greatly reduced in reconstructed images by the proposed method with the fuzzy bit plane.

Keywords: Image compression, Edge detection, Ground truth image, Peak signal to noise ratio

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Authors: Parvinder Singh Sandhu, Dalwinder Singh Salaria, Hardeep Singh

Abstract:

Software Reusability is primary attribute of software quality. There are metrics for identifying the quality of reusable components but the function that makes use of these metrics to find reusability of software components is still not clear. These metrics if identified in the design phase or even in the coding phase can help us to reduce the rework by improving quality of reuse of the component and hence improve the productivity due to probabilistic increase in the reuse level. In this paper, we have devised the framework of metrics that uses McCabe-s Cyclometric Complexity Measure for Complexity measurement, Regularity Metric, Halstead Software Science Indicator for Volume indication, Reuse Frequency metric and Coupling Metric values of the software component as input attributes and calculated reusability of the software component. Here, comparative analysis of the fuzzy, Neuro-fuzzy and Fuzzy-GA approaches is performed to evaluate the reusability of software components and Fuzzy-GA results outperform the other used approaches. The developed reusability model has produced high precision results as expected by the human experts.

Keywords: Software Reusability, Software Metrics, Neural Networks, Genetic Algorithm, Fuzzy Logic.

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Authors: Hamed.Vezvaei, Sepideh.Ordibeheshti, Mehdi.Ardjmand

Abstract:

Gasoline Octane Number is the standard measure of the anti-knock properties of a motor in platforming processes, that is one of the important unit operations for oil refineries and can be determined with online measurement or use CFR (Cooperative Fuel Research) engines. Online measurements of the Octane number can be done using direct octane number analyzers, that it is too expensive, so we have to find feasible analyzer, like ANFIS estimators. ANFIS is the systems that neural network incorporated in fuzzy systems, using data automatically by learning algorithms of NNs. ANFIS constructs an input-output mapping based both on human knowledge and on generated input-output data pairs. In this research, 31 industrial data sets are used (21 data for training and the rest of the data used for generalization). Results show that, according to this simulation, hybrid method training algorithm in ANFIS has good agreements between industrial data and simulated results.

Keywords: Adaptive Neuro-Fuzzy Inference Systems, GasolineOctane Number, Soft-sensor, Catalytic Naphtha Reforming

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