فهرست مطالب

fuzzy systems - Volume:11 Issue:1, 2014
  • Volume:11 Issue:1, 2014
  • 170 صفحه،
  • تاریخ انتشار: 1392/12/20
  • تعداد عناوین: 8
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  • Hongyun Yue, Junmin Li Page 1
    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 guarantees 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 proposed approach.
    Keywords: Fuzzy adaptive control, Nonlinear systems, Discrete, distributed time, varying delays, Backstepping, Lyapunov, Krasovskii functionals
  • Mohsen Zeinalkhani, Mahdi Eftekhari Page 27
    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.
    Keywords: Fuzzy inference systems, Classification, Fuzzy decision tree, Stopping criteria
  • Developing Fuzzy Models for Estimating the Quality of VoIP
    F. Rahdari, M. Eftekhari, A. Akbari, M. Zeinalkhani Page 49
    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.
    Keywords: VoIP, Voice quality, Non, intrusive prediction, PESQ, Neuro, fuzzy, GA, KEEL
  • Alireza Naghsh, Farid Sheikholeslam, Mohammad Danesh Page 75
    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.
    Keywords: Force, Position control, Adaptive, Fuzzy estimation, Robot manipulator, Stability
  • Maryam Mosleh Page 91
    In this paper, a novel hybrid method based on learning algorithm of fuzzy neural network and Newton-Cotes methods with positive coefficient for the solution of linear Fredholm integro-differential equation of the second kind with fuzzy initial value is presented. Here neural network is considered as a part of large field called neural computing or soft computing. We propose a learning algorithm from the cost function for adjusting fuzzyweights. This paper is one of the first attempts to derive learning algorithms from fuzzy neural networks with real input, fuzzy output, and fuzzy weights. Finally, we illustrate our approach by numerical examples.
    Keywords: Fuzzy neural networks, Fuzzy linear Fredholm integro, differential, Feedforward neural network, Learning algorithm
  • K. Abolpour, M. M. Zahedi Page 113
    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.
    Keywords: (Trim) Reversal general fuzzy automata, Active state set, (Coaccessible) Accessible general fuzzy recognizer, Join, Direct product
  • M. Otadi, M. Mosleh Page 135
    In this paper, we use the parametric form of fuzzy numbers, and an iterative approach for obtaining approximate solution for a class of fuzzy nonlinear Fredholm integral equations of the second kind is proposed. This paper presents a method based on Newton-Cotes methods with positive coefficient. Then we obtain approximate solution of the fuzzy nonlinear integral equations by an iterative approach.
    Keywords: Fuzzy nonlinear Fredholm integral equations, Newton, Cotes methods, Parametric form of a fuzzy number
  • Jin Xuan Fang, Hui Zhang Page 147
    In this paper, a new definition of bounded fuzzy linear order homomorphism on $I$-topological vector spaces is introduced. This definition differs from the definition of Fang [The continuity of fuzzy linear order-homomorphism. J. Fuzzy Math. {bf 5} textbf{(4)} (1997), 829–838]. We show that the ``boundedness «and `` boundedness on each layer» of fuzzy linear order homomorphisms do not imply each other. On the basis, characterizations of continuity of fuzzy linear order-homomorphisms, and the relation between continuity and boundedness are studied.
    Keywords: $I$, topological vector spaces, Bounded fuzzy set, Bounded fuzzy linear order, homomorphism