Sharif Digital Repository

Trip production has long been considered as a major element in trip demand estimation. Many models have been presented for this purpose. Models use socio-economic variables in order to predict trip production. This paper develops an Adaptive-Network-based Fuzzy Inference System (ANFIS) models to predict school trip production. ANFIS can construct an input-output mapping based on both human knowledge and stipulated input-output data pairs. In order to improve models' generalization capability, a heuristic algorithm is used to generate reasonable initial values for data loss in training data set. Models with different Membership Functions (MFs) were trained, validated and tested with real data... 

Supplier selection is an important and challenging problem in sustainable supply chain management. We propose a dynamic decision support system (DSS) for sustainable supplier selection in circular supply chains. Unlike the linear take-make-waste-dispose production systems, circular supply chains are nonlinear make-waste-recycle production systems with zero-waste vision. The proposed DSS allows users to customize and weight their economic, social, and circular criteria with a fuzzy best-worst method (BWM) and select the most suitable supplier with the fuzzy inference system (FIS). Machine learning is used to maintain and synthesize the criteria scores for the suppliers after each supplier... 

Supplier selection is an important and challenging problem in sustainable supply chain management. We propose a dynamic decision support system (DSS) for sustainable supplier selection in circular supply chains. Unlike the linear take-make-waste-dispose production systems, circular supply chains are nonlinear make-waste-recycle production systems with zero-waste vision. The proposed DSS allows users to customize and weight their economic, social, and circular criteria with a fuzzy best-worst method (BWM) and select the most suitable supplier with the fuzzy inference system (FIS). Machine learning is used to maintain and synthesize the criteria scores for the suppliers after each supplier... 

In this paper we develop a hybrid forecasting model which combines artificial intelligence and technical analysis to predict short-term stock price index. The results show that using technical indices as neural network's inputs yields good performance in forecasting short-term prices, but this model cannot predict long-term prices well. To overcome this shortcoming we have exploited a fuzzy inference system based on analyzing the historical effects of macro economic variables on the stock markets' indices. Our forecasting models differ from the other ones in two main aspects: the first one is analyzing previous macroeconomics trends in order to build a Mamdani FIS and the second one is... 

In cloud computing, services play key roles. Services are well defined and autonomous components. Nowadays, the demand of using Fuzzy inference as a service is increasing in the domain of complex and critical systems. In such systems, along with the development of the software, the cost of detecting and fixing software defects increases. Therefore, using formal methods, which provide clear, concise, and mathematical interpretation of the system, is crucial for the design of these Fuzzy systems. To obtain this goal, we introduce the Fuzzy Inference Cloud Service (FICS) and propose a novel discipline for formal modeling of the FICS. The FICS provides the service of Fuzzy inference to the... 

This paper presents a new and simple algorithm for partitioning the input domain for implementation of Active Learning Method (ALM) algorithm. ALM is a pattern-based algorithm for soft computing which uses the Ink Drop Spread (IDS) algorithm as its main engine for feature extraction. In this paper a simple algorithm is introduced with a few computation cost. In order to evaluate the performance of the proposed algorithm, it is applied to two applications, system modeling and pattern recognition. Simulation results show the effectiveness of our algorithm in specifying the appropriate points for dividing the inputs domains  

This paper introduces an innovative adaptive controller based on the actor-critic method. The proposed approach employs the ink drop spread (IDS) method as its main engine. The IDS method is a new trend in softcomputing approaches that is a universal fuzzy modeling technique and has been also used as a supervised controller. Its process is very similar to the processing system of the human brain. The proposed actor-critic method uses an IDS structure as an actor and a 2-dimensional plane, representing control variable states, as a critic that estimates the lifetime goodness of each state. This method is fast, simple, and away from mathematical complexity. The proposed method uses the... 

In this paper, a novel neuro-fuzzy computing system is proposed where its learning is based on the creation of fuzzy relations by using a new implication method without utilizing any exact mathematical techniques. Then, a simple memristor cross-bar-based analog circuit is designed to implement this neuro-fuzzy system which offers very interesting properties. In addition to high connectivity between neurons and being fault tolerant, all synaptic weights in our proposed method are always non-negative, and there is no need to adjust them precisely. Finally, this structure is hierarchically expandable, and it can do fuzzy operations in real time since it is implemented through analog circuits.... 

In a gas transmission network (GTN), faults can easily propagate due to the interconnections of streams. The main objective of this paper is to develop a systematic framework for an online decision support system (DSS) in order to make the right decisions to get the GTN out of critical conditions (which cannot be handled by the plant controllers) smoothly. One of the key features of the proposed scheme is its lack of dependence on prior knowledge of the fault signals (e.g., number of faults, and their origin). In this article, the GTN is modeled by a fuzzy directed graph (FDG). The proposed approach utilizes a reasoning algorithm based on the deviations that exist in the process variables... 

This study implements an adaptive neuro-fuzzy inference system (ANFIS) approach to predict the precipitation amount of the asphaltene using temperature (T), dilution ratio (Rv), and molecular weight of different n-alkanes. Results are then evaluated using graphical and statistical error analysis methods, confirming the model’s great ability for appropriate prediction of the precipitation amount. Mean squared error and determination coefficient (R2) values of 0.036 and 0.995, respectively are obtained for the proposed ANFIS model. Results are then compared to those from previously reported correlations revealing the better performance of the proposed model. © 2018, © 2018 Taylor & Francis... 

A new hybrid fuzzy adaptive algorithm for tracking maneuvering targets is proposed in this paper. The algorithm is implemented with fuzzy inference system (FIS) and current statistical model and adaptive Altering (CSMAF). The CSMAF algorithm is one of most effective methods for tracking the maneuvering targets. It has a higher precision in tracking the maneuvering targets with larger accelerations while it has a lower precision in tracking the maneuvering targets with smaller acceleration. In the proposed algorithm, to overcome the disadvantage of the CSMAF algorithm, the covariance of process noise CSMAF is adjusted adaptively by the output of a FIS. The input of the FIS is discrepancy of... 

Electromyogram signal (EMG) is an electrical manifestation of contractions of muscles. Surface EMG (sEMG) signal collected form surface of the skin has been used in diverse applications. One of its usages is exploiting it in a pattern recognition system which evaluates and synthesizes hand prosthesis movements. The ability of current prosthesis has been limited in simple opening and closing that decreases the efficacy of these devices in contrary to natural hand. In order to extend the ability and accuracy of prosthesis arm movements and performance, a novel approach for sEMG pattern recognizing system is proposed. In order to have a relevant comparison, present and recent research for... 

A new method for forecasting the trend of time series, based on mixture of MLP experts, is presented. In this paper, three neural network combining methods and an Adaptive Network-Based Fuzzy Inference System (ANFIS) are applied to trend forecasting in the Tehran stock exchange. There are two experiments in this study. In experiment I, the time series data are the Kharg petrochemical company's daily closing prices on the Tehran stock exchange. In this case study, which considers different schemes for forecasting the trend of the time series, the recognition rates are 75.97%, 77.13% and 81.64% for stacked generalization, modified stacked generalization and ANFIS, respectively. Using the... 

Fixed gain controllers for automatic generation control are designed at nominal operating conditions and fail to provide best control performance over a wide range of operating conditions. So, to keep system performance near its optimum, it is desirable to track the operating conditions and use updated parameters to compute control gains. A control scheme based on artificial neuro-fuzzy inference system (ANFIS), which is trained by the results of off-line studies obtained using particle swarm optimization, is proposed in this paper to optimize and update control gains in real-time according to load variations. Also, frequency relaxation is implemented using ANFIS. The efficiency of the... 

A novel method is proposed in the scope of image segmentation that solves this problem by breaking it into two main blocks. The first block's functionality is a method to anticipate the color basis of each segment in segmented images. One of the challenges of image segmentation is the inappropriate distribution of colors in the RGB color space. To determine the color of each segment, after mapping the input image onto the HSI color space, the image colors are classified into some clusters by exploiting the K-Means. Then, the list of cluster centers is winnowed down to a short list of colors based on a set of criteria. The second block of the proposed method defines how each pixel of the... 

In this work, a novel approach to model the dynamic behavior of the flash separation process (as a main building block of non-reacting stage-wise operations) based on Mamdani Fuzzy Inference Systems is proposed. This model surmounts the need to solve various types of mathematical equations governing the system and does not require thermodynamic properties which are either not available or computationally demanding. Hence it can be easily used in dynamic simulation of multi-phase flow in distributed systems. In the proposed approach the overall model is broken into several simple sub-models based on intuitive analysis of an expert. Moreover, a new fuzzy concept, named “Linguistic Composition... 

In this research, the fault-tolerant pitch angle control of a horizontal axis wind turbine in region 3 (where the wind velocity is greater than rated wind speed) is investigated. The effective wind velocity (EWV) is one of the necessary information for each control system. Wind speed is measured by the anemometers on the top of the nacelle however, the measurement is not precise and is only applicable for one point in the rotor. To address this issue, we have developed a novel hybrid approach. The approach is based on a sliding mode observer to estimate the aerodynamic torque and an adaptive neuro-fuzzy inference system (ANFIS) is introduced for obtaining the EWV. The estimated aerodynamic... 

In this investigation, a systematic sequential intelligent system is proposed to provide the surgeon with an estimation of the state of the tool-tissue interaction force in laparoscopic surgery. To train the proposed intelligent system, a 3D model of an in vivo porcine liver was built for different probing tasks. To capture the required knowledge, three different geometric features, i.e. Y displacement of the nodes on the upper surface and slopes on the closest node to the deforming area of the upper edge in both X-. Y and Z-. Y planes, were extracted experimentally. The numerical simulations are conducted in three independent successive stages. At the first step, a well-known... 

The main issue in a surveillance environment is the target tracking. The most important concern in this problem is the association of the various measurements with the existing target tracks. The fuzzy c-means data association (FCMDA) algorithm, based on the fuzzy c-means (FCM) algorithm, is an efficient solution for the problem of measurement to track association in a multisensor multitarget environment. It has a high accuracy in measurement to track association when targets are far from each other. However, its accuracy remains low when targets are close to one another. The FCMDA algorithm performance is usually lost in this environment, especially when measurement noise is high. In the... 

A novel UWB MIMO antenna with band-notched characteristic operating in the frequency range between 3.1 GHz and 10.6 GHz is presented. The designed MIMO antenna has two ports and the size of 32×14 mm2 which is fabricated on an FR-4 printed-circuit-board. The feeding system of the proposed antenna is a microstrip line. The two antennas are positioned face to face but laid reversely upon the substrate in order to have a good isolation, less cross polarization, high gain and good envelope correlation coefficient. S-parameters describe the input-output relationship between ports (or terminals) in a MIMO antenna. The effects of the strip feed line width on the variations in reflection coefficient...