Sharif Digital Repository

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... 

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... 

Automotive Industry has an important place all around the world and sales forecasting process supports companies to meet their goals such as sales revenue increase, efficiency improvement, capacity planning and customer care. Traditional methods such as time series and econometrics have been applied by scientists during last decades. However, recently sales forecast problem by means of machine learning techniques are welcomed by data scientists because of increasing power of information technology in both hardware and software aspects. In this research, the hybridization of clustering method, Adaptive network Fuzzy Inference System (ANFIS) and Particle Swarm Optimization (PSO) are developed... 

This paper introduces a novel approach for tuning the parameters of the adaptive network-based fuzzy inference system (ANFIS). In the commonly used training methods, the antecedent and consequent parameters of ANFIS are trained by gradient-based algorithms and recursive least square method, respectively. In this study, a new swarm-based meta-heuristic optimization algorithm, so-called "Cat Swarm Optimization", is used in order to train the antecedent part parameters and gradient descent algorithm is applied for training the consequent part parameters. Experimental results for prediction of Mackey-Glass model and identification of two nonlinear dynamic systems reveal that the performance of... 

This paper proposes a sparse distributed estimation algorithm when missing data occurs in the measurements over adaptive networks. Two classes of measurement models are considered. First, the traditional linear regression model is investigated and second the sign of the linear regression model is studied. The latter is referred to as one-bit model. We utilize the diffusion LMS strategy, in the proposed methods, where a set of nodes cooperates with each other to estimate a vector model parameter. In both models, it is shown that replacing the missing sample with a simple estimate is equivalent to removing the missing sample from the distributed diffusion algorithm. We consider two cases,... 

In this study, the distributed six degree-of-freedom (6-DOF) coordinated control of spacecraft formation flying in low earth orbit (LEO) has been investigated. For this purpose, an accurate coupled translational and attitude relative dynamics model of the spacecraft with respect to the reference orbit (virtual leader) is presented by considering the most effective perturbation acceleration forces on LEO satellites, i.e. the second zonal harmonic and the atmospheric drag. Subsequently, the 6-DOF coordinated control of spacecraft in formation is studied. During the mission, the spacecraft communicate with each other through a switching network topology in which the weights of its graph... 

The objective of this study was to model the extent of improvement in the degradability of phenol dyes by SnO2/Fe3O4 nanoparticles using a photocatalytic reactor. The effect of operative parameters including catalyst concentration, initial dye concentration, stirring intensity, and UV radiation intensity on the photocatalytic batch reactor during removal of phenol red was investigated. Fractional factorial design and response surface methodology were used to design the experiment layout. The SnO2/Fe3O4 nanoparticles were synthesized using the core-shell method. The results of x-ray diffraction and transmission electron microscopy showed the successful synthesis of these nanoparticles. The... 

In this study, the problem of variable bit rate (VBR) video traffic prediction is addressed. VBR traffic prediction is necessary in dynamic bandwidth allocation for multimedia quality of service control strategies. Autoregressive (AR) models have been widely used in VBR traffic prediction where the least mean square (LMS)-based methods were utilised for parameter estimation. However, they are ineffective when the traffic is dynamic in nature. In this study, using the Brock, Dechert, and Scheinkman (BDS) test, it is shown that the video traffic is non-linear. Kernel is an efficient tool to convert non-linear data into linear one in a higher-dimensional space. The kernel LMS (KLMS) method is... 

This paper presents a hybrid adaptive network based fuzzy inference system (ANFIS), computer simulation and time series algorithm to estimate and predict electricity consumption estimation. The difficulty with electricity consumption estimation modeling approach such as time series is the reason for proposing the hybrid approach of this study. The algorithm is ideal for uncertain, ambiguous and complex estimation and forecasting. Computer simulation is developed to generate random variables for monthly electricity consumption. Various structures of ANFIS are examined and the preferred model is selected for estimation by the proposed algorithm. Finally, the preferred ANFIS and time series... 

This paper considers the possibility and the details of implementing adaptive networks with visible light communication technology. Adaptive networks have various indoor and outdoor applications hence, their implementations with light waves face with different problems. Almost all free space optical (FSO)communication systems must deal with two channel deficiencies namely, optical noise and channel turbulence. Both channel noise and turbulence follow certain distributions that we explain in detail. In this paper, we analyze the effects of these disturbances on the estimation performance of incremental adaptive networks and present the results using various simulations. The results are... 

A new approach, based on Adaptive-Network-based Fuzzy Inference System (ANFIS), is presented for the classification of arthrometric data of normal/ACL-ruptured knees, considering the insufficiency of existing criteria. An ANFIS classifier was developed and tested on a total of 4800 arthrometric data points collected from 40 normal and 40 injured subjects. The system consisted of 5 layers and 8 rules, based on the results of subtractive data clustering, and trained using the hybrid algorithm method. The performance of the system was evaluated in four runs, in the framework of a 4-fold cross validation algorithm. The results indicated a definite correct diagnosis for typical injured and normal... 

The spatiotemporal dynamic of salinity concentration (SC) in saline lakes is strongly dependent on the rate of water flow into the lake, water circulation, wind speed, evaporation rate, and the phenomenon of salt precipitation and dissolution. Although in-situ observations most reliably quantify water quality metrics, the spatiotemporal distribution of such data are typically limited and cannot be readily extrapolated for either long-term projections or extensive areas. Alternatively, remotely sensed imagery has facilitated less expensive and a stronger ability to estimate water quality over a wide range of spatiotemporal resolutions. This study introduces an adaptive learning model that...