Sharif Digital Repository

This paper proposes an innovative robust-fuzzy method for multi-objective, multi-period supplier selection problem under multiple uncertainties. This approach integrates robust optimization and fuzzy programming. Uncertain parameters are modeled as random variables that take value within a symmetrical interval. However, due to the complexity or ambiguity of some real world problems and especially the nature of some of the available input data, the length of interval is also highly uncertain. This ambiguity motivated us to present a new approach, which could be applicable to multiple uncertainties conditions. Thus, in our approach, the half-length of these intervals is also represented by... 

Fuzzy techniques can be used for accurate and high-speed modeling as well as for the control of complex systems, but various challenging problems are usually encountered during their actual implementation. For example, the variable parameters need to be optimized iteratively during the training phase, where this process is inspired by crisp domain algorithms. However, in recent years, memristor-based structures have emerged as another promising method for implementing neural network structures and fuzzy algorithms. In this study, we propose a novel adaptive and real-time fuzzy modeling algorithm, which employs the active learning method concept to mimic the functionality of the brain's right... 

The objective of this research is to develop a practical research and development (R&D) portfolio selection model that addresses the effective R&D project valuation issue, while tackling R&D uncertainty in portfolio optimization. Fuzzy set theory is employed to capture and model the uncertain project information. To evade the well-known complexities of fuzzy real option valuation, the recently developed fuzzy pay-off method is used to more effectively valuate R&D projects. The resulting problem is formulated as a fuzzy zero-one integer programming model that handles uncertainty of input data in order to determine the optimal portfolio. Two satisfaction measures, which are based on... 

In this study Active Learning Method (ALM) as a novel fuzzy modeling approach is compared with optimized Support Vector Machine (SVM) using simple Genetic Algorithm (GA), as a well known datadriven model for long term simulation of daily streamflow in Karoon River. The daily discharge data from 1991 to 1996 and from 1996 to 1999 were utilized for training and testing of the models, respectively. Values of the Nash-Sutcliffe, Bias, R 2, MPAE and PTVE of ALM model with 16 fuzzy rules were 0.81, 5.5 m 3 s -1, 0.81, 12.9%, and 1.9%, respectively. Following the same order of parameters, these criteria for optimized SVM model were 0.8, -10.7 m 3 s -1, 0.81, 7.3%, and -3.6%, respectively. The... 

To reduce the semantic gap in the content based image retrieval (CBIR) systems we propose a fuzzy rule base approach. By submitting a query to the proposed system, it first extracts its low-level features and then checks its rule base for determining the proper weight vector for its distance measure. It then uses this weight vector to determine what images are more similar to the query image. For the training purpose, an algorithm is provided by which the system adjusts its fuzzy rules' parameters by gathering the trainers' opinions on which and how much the image pairs are relevant. For further improving the performance of the system, a feature space dimensionality reduction method is also... 

This paper is concerned with the disturbance attenuation problem of fuzzy large-scale systems which consist of N interconnected subsystems which are represented by Takagi-Sugeno fuzzy models. Using Lyapunov function and linear matrix inequalities (LMIs), a criterion is proposed to have a prescribed level of disturbance attenuation. A numerical example is given to illustrate the control design procedure and its effectiveness  

The state-space averaging applied to switched networks generally results in nonlinear systems. It is common to perform a small signal linearization about an operating point to obtain a linear system. When the variations in signals are large, e.g., in PFC rectifiers, the small signal approximation produces results that are susceptible to instability problems. In this paper a class of piecewise linear models merged by fuzzy system are introduced for PWM converters. The necessary and sufficient and conditions for stability of fuzzy models using fuzzy state-feedback controllers are given. The results obtained are illustrated with a buck-boost converter. The simulation and experimental results... 

This paper presents a high performance direct torque control (DTC) theme for the induction motor (IM). To solve those problems associated with conventional DTC, such as flux and torque ripple, variable switching frequency, inaccuracy in motor model and other parts of system. The Active Learning Method (ALM) is implemented on the DTC. In the Active Learning Method for information modeling, a method known as Ink Drop Spread (IDS) is used. The simulation results of DTC system based on ALM and the comparison of motor performance under the proposed control system with respect to those obtained under conventional DTC confirms its effectiveness and accuracy  

This research deals with a model with better efficiency for selection of portfolio making use of cardinal constraints, which are explained in previous sections. Such a method, which is a combination of fuzzy models and MCDM considering the constraints intended by investors, has not been used in previous models. We have considered transactions cost, because they are among factors important for an investor, and their being ignored in a portfolio selection method will result in inefficient portfolio. Sector value constraint is among other constraints considered here. Such a constraint aims to raise investment rate in sectors with higher values. Cardinal constraints (number of shares existing in... 

The purpose of this article is to develop a new method to estimate annual energy output for a given wind turbine in any region which should be easy to use and has satisfactory accuracy. To do this, hourly wind speeds of 25 different stations in Netherlands, output power curve of S47 wind turbine and fuzzy modeling techniques and artificial neural networks were used and a model is developed to estimate annual energy output for S47 wind turbine in different regions. Since this model has three inputs (average wind speed, standard deviation of wind speed, and air density of that region), this model is easy to use. The accuracy of this method is compared with the accuracy of conventional methods... 

The disguise of traditional monopolistic electricity markets into deregulated competitive ones has made 'price forecasting' a crucial strategy for both producers and consumers: for the producers, to maximize their profit and hedge against price volatilities and for the consumers to manage their utility. Electricity price forecasting has thus emerged as a progressive field of study and numerous researches have been conducted to improve and optimize the price forecast methods. This paper proposes a precise and computationally efficient method to perform price forecasting in deregulated power markets. A locally linear neuro-fuzzy model is developed for price forecasting. The model is trained by... 

Route choice modeling is one of the most important parts of traffic assignment problem. Recently, this model is used to describe the reactions of drivers to Traveler Information Systems in order to develop accurate Advanced Traffic Management and Information System (ATMIS). Therefore accurate model is necessary. In this paper we proposed a new model based on Type-II fuzzy logic to model route choice problem. This model can take account of the imprecision, uncertainties and vagueness lying in the dynamic choice process and makes more accurate modeling of drivers' behavior than deterministic, stochastic and Type-I fuzzy models. In our proposed model we consider average speed and cost... 

In this paper an accurate real-time sequence-based system for representation, recognition, interpretation, and analysis of the facial action units (AUs) and expressions is presented. Our system has the following characteristics: 1) employing adaptive-network-based fuzzy inference systems (ANFIS) and temporal information, we developed a classification scheme based on neuro-fuzzy modeling of the AU intensity, which is robust to intensity variations, 2) using both geometric and appearance-based features, and applying efficient dimension reduction techniques, our system is robust to illumination changes and it can represent the subtle changes as well as temporal information involved in formation... 

Magnetic flux leakage is the most widely used method for oil and gas pipeline non destructive testing. The saturation level of the sample under test has a significant effect on its efficiency; therefore, the magnetizing yoke requires an elaborate design. The finite element method is the conventional approach used for this purpose, but it is very time consuming. In this article, a neuro-fuzzy method is presented to model the behavior of the magnetizing yoke. Modeling a few different designs with the finite element method and using the results for training the neuro-fuzzy model eradicates the necessity of modeling a huge number of designs with the finite element method. The acquired... 

A novel hybrid algorithm based on a genetic algorithm and particle swarm optimization to design a fuzzy neural network, named self-organizing fuzzy neural network based on GA and PSO (SOFNNGAPSO), to implement Takagi-Sugeno (TS) type fuzzy models is proposed in this paper. The proposed algorithm, as a new hybrid algorithm, consists of two phases. A tuning based on TS's fuzzy model is applied to identify the fuzzy structure, and also a fuzzy cluster validity index is utilized to determine the optimal number of clusters. To obtain a more precision model, GA and PSO are performed to conduct fine tuning for the obtained parameter set of the premise parts and consequent parts in the... 

One of the most important parts of a cement factory is the cement rotary kiln which plays a key role in quality and quantity of produced cement. In this part, the physical exertion and bilateral movement of air and materials, together with chemical reactions take place. Thus, this system has immensely complex and nonlinear dynamic equations. These equations have not worked out yet. Only in exceptional case; however, a large number of the involved parameters were crossed out and an approximation model was presented instead. This issue caused many problems for designing a cement rotary kiln controller. In this paper, we presented nonlinear predictor and simulator models for a real cement... 

In this paper, we use nonlinear system identification method to predict and detect process fault of a cement rotary kiln. After selecting proper inputs and output, an input-output model is identified for the plant. To identify the various operation points in the kiln, Locally Linear Neuro-Fuzzy (LLNF) model is used. This model is trained by LOLIMOT algorithm which is an incremental tree-structure algorithm. Then, by using this method, we obtained 3 distinct models for the normal and faulty situations in the kiln. One of the models is for normal condition of the kiln with 15 minutes prediction horizon. The other two models are for the two faulty situations in the kiln with 7 minutes... 

In this paper by using a combination of fuzzy identification and the sliding mode control a fuzzy adaptive sliding mode scheme is designed to stabilize the unstable periodic orbits of chaotic systems. The chaotic system is assumed to have an affine form x(n) = f(X) + g(X)u where f and g are unknown functions. Using only the input-output data obtained from the underlying dynamical system, two fuzzy systems are constructed for identification of f and g. Two distinct methods are utilized for fuzzy modeling, the least squares and the gradient descent techniques. Based on the estimated fuzzy models, an adaptive controller, which works through the sliding mode control, is designed to make the... 

Trip distribution is considered as the second step in urban transportation planning. The important factors which affect trip distribution are the characteristics of origins and destinations and travel impedance between O/D. Trip distribution traditionally models with the deterministic variables although it seems affective variables in trip distribution molding are based on human perceptions. Since perceptions of people vary from one person to another, thus variables are imprecise and vague. Fuzzy approaches are proper tools of modeling non-deterministic variables. In this paper we present fuzzy estimation models of trip distribution for discretionary trip purposes including: shopping,... 

In this paper, a combination of fuzzy clustering estimation and sliding mode control is used to control a chaotic system, which its mathematical model is unknown. It is assumed that the chaotic system has an affine form. At first, the nonlinear noninput part of the chaotic system is estimated by a fuzzy model, without using any input noise signal. Without loss of generality, it is assumed that chaotic behavior is appeared in the absence of input signal. In this case, the recurrent property of chaotic behavior is used for estimating its model. After constructing the fuzzy model, which estimates the noninput part of the chaotic system, control and on-line identification of the input-related...