2018-12-15T17:42:50Z
http://ijfs.usb.ac.ir/?_action=export&rf=summon&issue=240
Iranian Journal of Fuzzy Systems
IJFS
1735-0654
1735-0654
2014
11
1
Cover vol. 11, no. 1, February 2014
2014
03
01
0
http://ijfs.usb.ac.ir/article_2691_0de979703efb6c6126122e5c4eafa558.pdf
Iranian Journal of Fuzzy Systems
IJFS
1735-0654
1735-0654
2014
11
1
ADAPTIVE FUZZY TRACKING CONTROL FOR A CLASS OF NONLINEAR SYSTEMS WITH UNKNOWN DISTRIBUTED TIME-VARYING DELAYS AND UNKNOWN CONTROL DIRECTIONS
Hongyun
Yue
Junmin
Li
In 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.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 guaranteesthat 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 theproposed approach.
Fuzzy adaptive control
Nonlinear systems
Discrete and distributed time-varying delays
Backstepping
Lyapunov-Krasovskii functionals
2014
02
24
1
25
http://ijfs.usb.ac.ir/article_1393_5998db6dda90762fa01867861621467b.pdf
Iranian Journal of Fuzzy Systems
IJFS
1735-0654
1735-0654
2014
11
1
Comparing different stopping criteria for fuzzy decision tree induction through IDFID3
Mohsen
Zeinalkhani
Mahdi
Eftekhari
Fuzzy 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.
Fuzzy inference systems
Classification
Fuzzy decision tree
Stopping criteria
2014
02
25
27
48
http://ijfs.usb.ac.ir/article_1394_2ba9daf85e10827d238087b91e686c5c.pdf
Iranian Journal of Fuzzy Systems
IJFS
1735-0654
1735-0654
2014
11
1
Developing Fuzzy Models for Estimating the Quality of VoIP
F.
Rahdari
M.
Eftekhari
A.
Akbari
M.
Zeinalkhani
This 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.
VoIP
Voice quality
Non-intrusive prediction
PESQ
Neuro-fuzzy
GA
KEEL
2014
02
25
49
73
Iranian Journal of Fuzzy Systems
IJFS
1735-0654
1735-0654
2014
11
1
Design of an Adaptive Fuzzy Estimator for Force/Position Tracking in Robot Manipulators
Alireza
Naghsh
Farid
Sheikholeslam
Mohammad
Danesh
This 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.
Force/Position control
Adaptive
Fuzzy estimation
Robot manipulator
stability
2014
02
25
75
89
http://ijfs.usb.ac.ir/article_1396_5573c6189fbc54601bee32594d6b9478.pdf
Iranian Journal of Fuzzy Systems
IJFS
1735-0654
1735-0654
2014
11
1
Numerical solution of fuzzy linear Fredholm integro-differential equation by \fuzzy neural network
Maryam
Mosleh
In this paper, a novel hybrid method based on learning algorithmof fuzzy neural network and Newton-Cotesmethods with positive coefficient for the solution of linear Fredholm integro-differential equation of the second kindwith fuzzy initial value is presented. Here neural network isconsidered as a part of large field called neural computing orsoft computing. We propose alearning algorithm from the cost function for adjusting fuzzyweights. This paper is one of the first attempts to derive learningalgorithms from fuzzy neural networks with real input, fuzzy output,and fuzzy weights. Finally, we illustrate our approach by numerical examples.
Fuzzy neural networks
Fuzzy linear Fredholm
integro-differential
Feedforward neural network
Learning algorithm
2014
02
25
91
112
http://ijfs.usb.ac.ir/article_1397_1e44bcaee4c0a281eafa46bae01a2651.pdf
Iranian Journal of Fuzzy Systems
IJFS
1735-0654
1735-0654
2014
11
1
Language of General Fuzzy Recognizer
K.
Abolpour
M. M.
Zahedi
In 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.
(Trim) Reversal general fuzzy automata
Active state set
(Coaccessible) Accessible general fuzzy recognizer
Join
Direct product
2014
02
25
113
134
http://ijfs.usb.ac.ir/article_1398_998e413e86612781f35da097555d3bf4.pdf
Iranian Journal of Fuzzy Systems
IJFS
1735-0654
1735-0654
2014
11
1
Numerical solutions of fuzzy nonlinear integral equations of the second kind
M.
Otadi
M.
Mosleh
In this paper, we use the parametric form of fuzzy numbers, and aniterative approach for obtaining approximate solution for a classof fuzzy nonlinear Fredholm integral equations of the second kindis proposed. This paper presents a method based on Newton-Cotesmethods with positive coefficient. Then we obtain approximatesolution of the fuzzy nonlinear integral equations by an iterativeapproach.
Fuzzy nonlinear Fredholm integral equations
Newton-Cotes methods
Parametric form of a fuzzy number
2014
02
25
135
145
http://ijfs.usb.ac.ir/article_1399_b0bad9e9b8effa0799936bdd34f47202.pdf
Iranian Journal of Fuzzy Systems
IJFS
1735-0654
1735-0654
2014
11
1
Boundedness and Continuity of Fuzzy Linear Order-Homomorphisms on $I$-Topological\ Vector Spaces
Jin Xuan
Fang
Hui
Zhang
In this paper, a new definition of bounded fuzzy linear orderhomomorphism on $I$-topological vector spaces is introduced. Thisdefinition differs from the definition of Fang [The continuity offuzzy linear order-homomorphism. J. Fuzzy Math. {bf5}textbf{(4)}(1997), 829--838]. We show that the ``boundedness"and `` boundedness on each layer" of fuzzy linear orderhomomorphisms do not imply each other. On the basis,characterizations of continuity of fuzzy linearorder-homomorphisms, and the relation between continuity andboundedness are studied.
$I$-topological vector spaces
Bounded fuzzy set
Bounded fuzzy
linear order-homomorphism
2014
02
25
147
157
http://ijfs.usb.ac.ir/article_1400_24ff2fd4695152cdc861a3e7cec945fe.pdf
Iranian Journal of Fuzzy Systems
IJFS
1735-0654
1735-0654
2014
11
1
Persian-translation vol. 11, no. 1, February 2014
2014
03
01
161
168
http://ijfs.usb.ac.ir/article_2692_f0227535d3d0e8df719bab08ce5f3bc8.pdf