Sharif Digital Repository

In this study the ability of artificial neural network (ANN) in forecasting the daily NASDAQ stock exchange rate was investigated. Several feed forward ANNs that were trained by the back propagation algorithm have been assessed. The methodology used in this study considered the short-term historical stock prices as well as the day of week as inputs. Daily stock exchange rates of NASDAQ from January 28, 2015 to 18 June, 2015 are used to develop a robust model. First 70 days (January 28 to March 7) are selected as training dataset and the last 29 days are used for testing the model prediction ability. Networks for NASDAQ index prediction for two type of input dataset (four prior days and nine... 

In [1], an optimal midcourse guidance law for close distances, where the difference of gravity for interceptor and ballistic missile is negligible, was introduced. There, a closed form solution, based on an optimization problem, was found, with very good performance for close distances but degraded performance in real problems with unequal gravity for missile and interceptor. In this paper, the difference of gravity is taken into account by considering a spherical gravity model. A new equation to express the relative motion between missile and interceptor is used to derive a "Near Optimal Guidance Law". The results found using the new derivation are similar to those in [1] but it provides a... 

Every year, the health of millions of people around the world is compromised by misdiagnosis, which sometimes could even lead to death. In addition, it entails huge financial costs for patients, insurance companies, and governments. Furthermore, many physicians’ professional life is adversely affected by unintended errors in prescribing medication or misdiagnosing a disease. Our aim in this paper is to use data mining methods to find knowledge in a dataset of medical prescriptions that can be effective in improving the diagnostic process. In this study, using 4 single classification algorithms including decision tree, random forest, simple Bayes, and K-nearest neighbors, the disease and its... 

Special attention is given to vertical takeoff and landing air vehicles due to their unique capabilities and versatile missions. The main problem here is control effectiveness at low flight speeds and transition maneuvers because of the inherent instability. RMIT is a small sized tail-sitter ducted fan air vehicle with a particular configuration layout, multiple control surfaces, low weight, and high-speed flight capability. In the current study, a comprehensive nonlinear model is firstly developed for RMIT, followed by a validation process. This model consists of all parts including aerodynamic forces and moments, control surfaces term together with the gravity and driving fan forces.... 

This Letter tries to find the area in parameter space of a non-salient Permanent-Magnet Synchronous Machine (PMSM) in which chaos can occur. This area is briefly named as chaotic area. The predicted chaotic area is obtained by checking some conditions which are necessary for existence of chaos in a dynamical system. In this Letter, it is assumed that this machine is in the generator mode, and its model is based on direct and quadrature axis of stator voltages and currents. The information of the predicted area is used in non-chaotic maximum power control of torque in the machine  

Over the last two decades there have been numerous reports from different seismically active regions of the world that thermal infrared (TIR) anomalies can be identified around the epicentral areas before major earthquakes [e.g. (Tronin et al., 2002)]. The TIR anomalies reportedly appear as early as 14 to 7 days before the seismic events and affect areas as large as 1000s to 100,000s km2 in size. Our case study for detection of TIRs using NOAA-AVHRR data(Band 4) is an Ms = 5.1 earthquake that occurred on 14th October 2004 near Ravar in Kerman province located in Loot and Tabas deserts, southeast-central Iran. The area is part of the Golbaf-Sirj seismogenic zone. It includes major faults... 

During last 20 years, neuroimaging with functional magnetic resonance imaging (fMRI) in people with drug addictions has introduced a wide range of quantitative biomarkers from brain's regional or network level activities during different cognitive functions. These quantitative biomarkers could be potentially used for assessment, planning, prediction, and monitoring for "addiction medicine" during screening, acute intoxication, admission to a program, completion of an acute program, admission to a long-term program, and postgraduation follow-up. In this chapter, we have briefly reviewed main neurocognitive targets for fMRI studies associated with addictive behaviors, main study types using... 

The power quality has become a major concern since late 1980's. There has been increasing interest in studying power quality problems, one of which is the existence of the current/voltage harmonics. In order to eliminate these harmonics, different methods have so far been proposed and developed. In this paper, line current harmonics reduction is achieved by using an active filter. More specifically, our aim is to introduce a proper control design method for the current control part of the active filter. To achieve the goal, two different predictive controllers are examined. Due to the existing physical constraints (switching frequency and computational limits) in real applications, a new... 

This paper investigates the control of diesel engines comprising two actuators: exhaust gas recirculation (EGR) valve and variable geometry turbocharger (VGT) to mitigate the exhaust emissions. In contrast to conventional control techniques, multi-model predictive control (MMPC) algorithm is employed for control purposes. With the help of proposed scheme, constraints of the control problem is also considered and nonlinear model to enhance performance is utilized. In doing so, new controller tries to fulfill a compromise between fuel consumption and torque response by regulating air-to-fuel ratio and EGR fraction to decrease pollutants emission especially nitrogen oxides (NOx). The dynamic... 

PM10 prediction has attracted special legislative and scientific attention due to its harmful effects on human health. Statistical techniques have the potential for high-accuracy PM10 prediction and accordingly, previous studies on statistical methods for temporal, spatial and spatio-temporal prediction of PM10 are reviewed and discussed in this paper. A review of previous studies demonstrates that Support Vector Machines, Artificial Neural Networks and hybrid techniques show promise for suitable temporal PM10 prediction. A review of the spatial predictions of PM10 shows that the LUR (Land Use Regression) approach has been successfully utilized for spatial prediction of PM10 in urban areas.... 

The current methods of predictive controllers are utilized for those processes in which the rate of output variations is not high. For such processes, therefore, stability can be achieved by implementing the constrained predictive controller or applying infinite prediction horizon. When the rate of the output growth is high (e.g. for unstable nonminimum phase process) the stabilization seems to be problematic. In order to avoid this, it is suggested to change the method in the way that: first, the prediction error growth should be decreased at the early stage of the prediction horizon, and second, the rate of the error variation should be penalized. The growth of the error is decreased... 

Predictive functional controller originally developed based on a first order reference model. In this paper the related equation of the predictive functional control is derived for an arbitrary reference model and then a simple approach to tune different parameters of the controller is examined by applying the controller on a benchmark problem. Finally, the robustness of the design is improved using the so called Youla parameterization approach. © 2005 IEEE  

In this paper, a hierarchical predictive controller is designed in order to solve the tracking problem of a moving ground target by a quad-rotor in an unknown and uneven environment. This controller has internal and external predictive controller levels. In the lower layer of the controller, a constrained predictive controller is designed that is capable of rejecting perturbations and quickly tracking the reference path, and in the outer loop, a model predictive controller is designed to optimally detect the moving ground target where, the sub-cost functions were defined so that the quad-rotor would be able to track the moving ground target even if it was temporarily out of sight of the... 

In this paper, a hierarchical predictive controller is designed in order to solve the tracking problem of a moving ground target by a quad-rotor in an unknown and uneven environment. This controller has internal and external predictive controller levels. In the lower layer of the controller, a constrained predictive controller is designed that is capable of rejecting perturbations and quickly tracking the reference path, and in the outer loop, a model predictive controller is designed to optimally detect the moving ground target where, the sub-cost functions were defined so that the quad-rotor would be able to track the moving ground target even if it was temporarily out of sight of the... 

This paper describes an application of intelligence- based predictive scheme to load-frequency control (LFC) in a two-area interconnected power system. In this investigation, at first, a dynamic model of the present system has to be considered and subsequently an efficient control scheme which is organized based on Takagi-Sugeno-Kang (TSK) fuzzy-based scheme and linear generalized predictive control (LGPC) scheme needs to be developed. In the control scheme proposed, frequency deviation versus load electrical power variation could efficiently be dealt with, at each instant of time. In conclusion, in order to validate the effectiveness of the proposed control scheme, the whole of outcomes are... 

Presented in this paper are a method, named the Dynamic Rule Prediction (DRP), which predicts the behavior of a system and its application in designing a controller. The aim of the study is to overcome some of the limitations and shortcoming of the other modeling methods. The effectiveness of this method is verified. The controller based on DRP possesses two main features. It can control the system without any prior knowledge of the controlled plant. It is, also, superior as its high-speed prediction. This paper focuses on the robot manipulator controllers and applications of this approach in it  

Self compacting concrete (SCC), as an innovative construction material in concrete industry, offers a safer and more productive construction process due to favorable rheological performance which is caused by SCC's different mixture composition. This difference may have remarkable influence on the mechanical behavior of SCC as compared to normal vibrated concrete (NVC) in hardened state. Therefore, it is vital to know whether the use of all assumptions and relations that have been formulated for NVC in current design codes are also valid for SCC. Furthermore, this study presents an extensive evaluation and comparison between mechanical properties of SCC using current international codes and... 

Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a... 

To deal with strong nonlinearity in nonlinear systems, a new method called cascade multiple-model predictive controller based on gap metric and stability margin, is proposed. The gap metric is utilized to describe the nonlinear system by a linear model bank. It is possible to select nominal local models from the linear model bank by an algorithm based on the gap metric and stability margin to avoid the redundancy of the local controllers. By scheduling proportional controller for each nominal local model, the robust stability is guaranteed whereas there will be no guarantee for the desired performance. Then, by designing a model predictive controller in the cascade structure, the closed loop... 

Nowadays, Programmable Electronic AC Load (PEAL) is widely used to test various devices such as solar inverters, UPSs, filters, generators, and generally test all AC power and measuring devices. In this paper, Model Predictive Current Control (MPCC) is used for control of a programmable AC load. In each period, switching state that minimizes the cost function is selected and applied to the converter. Cost function is the square of the current components error. The effect of the horizon of prediction on the quality of the load current is investigated. To decrease the calculations burden, a limited search pool is used. Simulation results confirm that using two steps prediction horizon with...