University of Sistan and BaluchestanIranian Journal of Fuzzy Systems1735-065411120140301Cover vol. 11, no. 1, February 20140269110.22111/ijfs.2014.2691ENJournal Article20161017http://ijfs.usb.ac.ir/article_2691_0de979703efb6c6126122e5c4eafa558.pdfUniversity of Sistan and BaluchestanIranian Journal of Fuzzy Systems1735-065411120140224ADAPTIVE FUZZY TRACKING CONTROL FOR A CLASS OF NONLINEAR SYSTEMS WITH UNKNOWN DISTRIBUTED TIME-VARYING DELAYS AND UNKNOWN CONTROL DIRECTIONS125139310.22111/ijfs.2014.1393ENHongyunYueDepartment of Applied Mathematics, Xidian University, Xi'an 710071,
P.R.ChinaJunminLiDepartment of Applied Mathematics, Xidian University, Xi'an 710071,
P.R.ChinaJournal Article20120224In this paper, an adaptive fuzzy control scheme is proposed for a class of perturbed strict-feedback nonlinear systems with unknown discrete and distributed time-varying delays, and the proposed design method does not require a priori knowledge of the signs of the control gains.<br />Based on the backstepping technique, the adaptive fuzzy controller is constructed. The main contributions of the paper are that (i) by constructing appropriate Lyapunov functionals and using the Nussbaum functions, the adaptive tracking control problem is solved for the strict-feedback unknown nonlinear systems with the unknown discrete and distributed time-varying delays and the unknown control directions (ii) the number of adaptive parameters is independent of the number of rules of fuzzy logic systems and system state variables, which reduces the computation burden greatly. It is proven that the proposed controller guarantees<br />that all the signals in the closed-loop system are bounded and the system output converges to a small neighborhood of the desired reference signal. Finally, an example is used to show the effectiveness of the<br />proposed approach.http://ijfs.usb.ac.ir/article_1393_5998db6dda90762fa01867861621467b.pdfUniversity of Sistan and BaluchestanIranian Journal of Fuzzy Systems1735-065411120140225Comparing different stopping criteria for fuzzy decision tree induction through IDFID32748139410.22111/ijfs.2014.1394ENMohsenZeinalkhaniDepartment of Computer Engineering, Shahid Bahonar Uni-
versity of Kerman, Kerman, IranMahdiEftekhariDepartment of Computer Engineering, Shahid Bahonar University
of Kerman, Kerman, IranJournal Article20120126Fuzzy Decision Tree (FDT) classifiers combine decision trees with approximate reasoning offered by fuzzy representation to deal with language and measurement uncertainties. When a FDT induction algorithm utilizes stopping criteria for early stopping of the tree's growth, threshold values of stopping criteria will control the number of nodes. Finding a proper threshold value for a stopping criterion is one of the greatest challenges to be faced in FDT induction. In this paper, we propose a new method named Iterative Deepening Fuzzy ID3 (IDFID3) for FDT induction that has the ability of controlling the tree’s growth via dynamically setting the threshold value of stopping criterion in an iterative procedure. The final FDT induced by IDFID3 and the one obtained by common FID3 are the same when the numbers of nodes of induced FDTs are equal, but our main intention for introducing IDFID3 is the comparison of different stopping criteria through this algorithm. Therefore, a new stopping criterion named Normalized Maximum fuzzy information Gain multiplied by Number of Instances (NMGNI) is proposed and IDFID3 is used for comparing it against the other stopping criteria. Generally speaking, this paper presents a method to compare different stopping criteria independent of their threshold values utilizing IDFID3. The comparison results show that FDTs induced by the proposed stopping criterion in most situations are superior to the others and number of instances stopping criterion performs better than fuzzy information gain stopping criterion in terms of complexity (i.e. number of nodes) and classification accuracy. Also, both tree depth and fuzzy information gain stopping criteria, outperform fuzzy entropy, accuracy and number of instances in terms of mean depth of generated FDTs.http://ijfs.usb.ac.ir/article_1394_2ba9daf85e10827d238087b91e686c5c.pdfUniversity of Sistan and BaluchestanIranian Journal of Fuzzy Systems1735-065411120140225Developing Fuzzy Models for Estimating the Quality of VoIP4973139510.22111/ijfs.2014.1395ENF.RahdariComputer and Information Technology Department, Institute of Sci-
ence and High Technology and Environmental Sciences, Graduate University of Ad-
vanced Technology, Kerman, IranM.EftekhariComputer Engineering Department, Shahid Bahonar University of
Kerman, Kerman, IranA.AkbariComputer Engineering Department, Iran University of Science and Tech-
nology, Tehran, IranM.ZeinalkhaniComputer Engineering Department, Shahid Bahonar University of
Kerman, Kerman, IranJournal Article20111226This paper presents a novel method for modeling the one-way quality prediction of VoIP, non-intrusively. Intrusive measures of voice quality suffer from common deficiency that is the need of reference signal for evaluating the quality of voice. Owing to this lack, a great deal of effort has been recently devoted for modeling voice quality prediction non-intrusively according to quality degradation parameters, while among the past proposed methods, intelligent techniques have been remarkably successful due to their abilities for modeling the non-linear processes. The present study introduces a procedure for developing fuzzy models, employing Genetic Algorithm (GA) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The proposed method is able to generate optimized fuzzy models in terms of accuracy and complexity. The efficiency of this procedure is compared with and contrasted against 13 regression methods implemented in KEEL as one machine learning tool. Moreover, several experimental results are performed over voice data from 10 different languages. In order to complete the experiment, a comprehensive statistical comparison is also drawn between our proposed method and other previous ones. The results apparently show the efficiency and applicability of this novel method in terms of generating accurate and simple fuzzy models for estimating the VoIP quality.University of Sistan and BaluchestanIranian Journal of Fuzzy Systems1735-065411120140225Design of an Adaptive Fuzzy Estimator for Force/Position Tracking in Robot Manipulators7589139610.22111/ijfs.2014.1396ENAlirezaNaghshDepartment of Engineering, Science and Research Branch, Islamic
Azad University, Tehran, IranFaridSheikholeslamDepartment of Electrical and Computer Engineering, Isfahan
University of Technology, Isfahan, 84156-83111, IranMohammadDaneshDepartment of Mechanical Engineering, Isfahan University of
Technology, Isfahan, 84156-83111, IranJournal Article20111126This paper presents a stable new algorithm for force/position control in robot manipulators. In this algorithm, position vectors are measured by sensors and then used in the control law. Since using force sensor has some issues such as high costs and technical problems, an approach is presented to overcome these issues. In this respect, force sensor is replaced by an adaptive fuzzy estimator to estimate the external force based on position and velocity measurements. In this approach, force can be properly estimated using universal approximation property of fuzzy systems. Therefore, robots can be controlled in different environments even when no exact mathematical model is available. Since this approach is adaptive, accuracy of the system can be improved with time. Through a theorem the stability of the control system is analyzed using Lyapunov direct method. At last, satisfactory performances of the proposed approach are verified via some numerical simulations and the results are compared with some previous approaches.http://ijfs.usb.ac.ir/article_1396_5573c6189fbc54601bee32594d6b9478.pdfUniversity of Sistan and BaluchestanIranian Journal of Fuzzy Systems1735-065411120140225Numerical solution of fuzzy linear Fredholm integro-differential equation by \fuzzy neural network91112139710.22111/ijfs.2014.1397ENMaryamMoslehDepartment of Mathematics, Firoozkooh Branch, Islamic Azad Uni-
versity, Firoozkooh, IranJournal Article20111026In this paper, a novel hybrid method based on learning algorithm<br />of fuzzy neural network and Newton-Cotes<br />methods with positive coefficient for the solution of linear Fredholm<br /> integro-differential equation of the second kind<br />with fuzzy initial value is presented. Here neural network is<br />considered as a part of large field called neural computing or<br />soft computing. We propose a<br />learning algorithm from the cost function for adjusting fuzzy<br />weights. This paper is one of the first attempts to derive learning<br />algorithms from fuzzy neural networks with real input, fuzzy output,<br />and fuzzy weights. Finally, we illustrate our approach by numerical examples.http://ijfs.usb.ac.ir/article_1397_1e44bcaee4c0a281eafa46bae01a2651.pdfUniversity of Sistan and BaluchestanIranian Journal of Fuzzy Systems1735-065411120140225Language of General Fuzzy Recognizer113134139810.22111/ijfs.2014.1398ENK.AbolpourDepartment of Mathematics, Kazerun Branch, Islamic Azad Univer-
sity, Kazerun, IranM. M.ZahediDepartment of Mathematics, Shahid Bahonar University of Kerman,
Kerman, IranJournal Article20110801In this note first by considering the notion of general fuzzy automata (for simplicity GFA), we define the notions of direct product, restricted direct product and join of two GFA. Also, we introduce some operations on (Fuzzy) sets and then prove some related theorems. Finally we construct the general fuzzy recognizers and recognizable sets and give the notion of (trim) reversal of a given GFA. In particular, we define the notion of the language of a given general fuzzy $Sigma$-recognizer and we show that the language of direct product of two $Sigma$-recognizer is equal to direct product of their languages.http://ijfs.usb.ac.ir/article_1398_998e413e86612781f35da097555d3bf4.pdfUniversity of Sistan and BaluchestanIranian Journal of Fuzzy Systems1735-065411120140225Numerical solutions of fuzzy nonlinear integral equations of the second kind135145139910.22111/ijfs.2014.1399ENM.OtadiDepartment of Mathematics, Firoozkooh Branch, Islamic Azad Univer-
sity, Firoozkooh, IranM.MoslehDepartment of Mathematics, Firoozkooh Branch, Islamic Azad Univer-
sity, Firoozkooh, IranJournal Article20120101In this paper, we use the parametric form of fuzzy numbers, and an<br />iterative approach for obtaining approximate solution for a class<br />of fuzzy nonlinear Fredholm integral equations of the second kind<br />is proposed. This paper presents a method based on Newton-Cotes<br />methods with positive coefficient. Then we obtain approximate<br />solution of the fuzzy nonlinear integral equations by an iterative<br />approach.http://ijfs.usb.ac.ir/article_1399_b0bad9e9b8effa0799936bdd34f47202.pdfUniversity of Sistan and BaluchestanIranian Journal of Fuzzy Systems1735-065411120140225Boundedness and Continuity of Fuzzy Linear Order-Homomorphisms on $I$-Topological\ Vector Spaces147157140010.22111/ijfs.2014.1400ENJin XuanFangSchool of Mathematical Science, Nanjing Normal University, Nan-
jing, Jiangsu 210023, P. R. ChinaHuiZhangDepartment of Mathematics, Anhui NormalUniversity, Wuhu, Anhui 241000,
P. R. ChinaJournal Article20120301In this paper, a new definition of bounded fuzzy linear order<br />homomorphism on $I$-topological vector spaces is introduced. This<br />definition differs from the definition of Fang [The continuity of<br />fuzzy linear order-homomorphism. J. Fuzzy Math. {bf<br />5}textbf{(4)}(1997), 829--838]. We show that the ``boundedness"<br />and `` boundedness on each layer" of fuzzy linear order<br />homomorphisms do not imply each other. On the basis,<br />characterizations of continuity of fuzzy linear<br />order-homomorphisms, and the relation between continuity and<br />boundedness are studied.http://ijfs.usb.ac.ir/article_1400_24ff2fd4695152cdc861a3e7cec945fe.pdfUniversity of Sistan and BaluchestanIranian Journal of Fuzzy Systems1735-065411120140301Persian-translation vol. 11, no. 1, February 2014161168269210.22111/ijfs.2014.2692ENJournal Article20161017http://ijfs.usb.ac.ir/article_2692_f0227535d3d0e8df719bab08ce5f3bc8.pdf