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    Chaos Control in Delayed phase Space Constructed by the Takens' Embedding Theory

    , M.Sc. Thesis Sharif University of Technology Hajiloo, Reza (Author) ; Salarieh, Hassan (Supervisor) ; Alasty, Aria (Supervisor)
    One of the most surprising lessons of dynamical systems theory is that the phase spaces of simple nonlinear systems may contain strange attractors. For certain simple systems, such as pendulum, direct observations of the phase space and finding strange attractors are possible. But physical systems in which all the relevant dynamical variables can be simultaneously monitored are relatively scarce. Takens’ theory shows how a time-series of measurements of a single observable state can be often used to reconstruct qualitative features of the phase space of the system. In this research, using the time-series of one measurable state, an algorithm is proposed to control chaos. The approach... 

    Chaos Control in Continuous Time Systems Using Delayed Phase Space Constructed by Takens’ Embedding Theory

    , M.Sc. Thesis Sharif University of Technology Kaveh, Hojjat (Author) ; Salarieh, Hassan (Supervisor)
    This research has dedicated to study the control of chaos when the system dynamics is unknown and there are some limitations on measuring states. There are many chaotic systems with these features occurring in many biological, economical and mechanical systems. The usual chaos control methods do not have the ability to present a systematic control method for these kinds of systems. To fulfill these strict conditions we have employed Takens embedding theory which guarantees the preservation of topological characteristics of the chaotic attractor under an embedding named "Takens transformation". Takens transformation just needs time series of one of the measurable states. This transformation... 

    Short Term Traffic State Forecasting for Travel Time Estimation

    , M.Sc. Thesis Sharif University of Technology Badrestani, Ebrahim (Author) ; Beigy, Hamid (Supervisor)
    Real-time travel time estimation is a major requirement in many transportation related systems. One of the main challeges is to estimate the traffic speed and then forecast it for a short time. A valuable data source for this task is instant location of moving cars that is captured using global positioning system (GPS) and sent through internet in online manner. The main problem is that the resulting traffic data is severely sparse and also contains a lot of noise. Previous researchs on this type of data are mostly based on matrix or tensor factorization. In this work it is shown that despite the large fraction of missing value it is possible to use neural network for this problem with some... 

    Short-term Load Forecasting

    , M.Sc. Thesis Sharif University of Technology Shokuhian, Hamideh (Author) ; Fatemi Ardestani, Farshad (Supervisor) ; Barakchian, Mahdi (Supervisor)
    In this thesis we are going to forecast the hourly consumption of the electricity over the country with two models and then, combine them. The first model decomposes the consumption to a deterministic trend and a stochastic residual. The second one assumes that the trend part is also stochastic.Once the consumption is being predicted separately by the models, in the second part of the thesis, we will combine the results to get a final prediction. This prediction is going to be compared with the load forecast of the Dispatching Unit of the electricity network as a base model. We are going to answer two important questions: firstly, does combining the models give a better prediction or not,... 

    Forecasting P/E Ratio by Decomposing into Constituent Factros

    , M.Sc. Thesis Sharif University of Technology Lotfi, Ali (Author) ; Zamani, Shiva (Supervisor) ; Abdoh Tabrizi, Hossein (Supervisor)
    P/E ratio is studied in four levels in this study:
    1)Macroeconomics level
    2)Capital market level
    3)Industry level
    4)Company level
    The first level studies effects of macroeconomics variables on P/E ratio. At this level we use variables such as economic growth, inflation, exchange rate, and etc.The next level uses capital market variables such as market volume, and IPO information.The third level that we study in this research is industry level. Stocks of an industry generally behave similar, because they have common advantages and disadvantages, thus industry is an effective factor on P/E ratio.The last level studies financial statements and internal features of a... 

    Forecasting P/E Ratio Using Neural Networks

    , M.Sc. Thesis Sharif University of Technology Darvishan, Majid (Author) ; ahramgiri, Mohsen (Supervisor)
    This thesis firstly studies the parameters affecting P/E ratio. These parameters vary from Macroeconomics level, Economic growth and Inflation, to company level. Then this study deploys Neural Networks to predict magnitude of P/E and change direction of P/E ratio. To increase accuracy, thesis uses three different method of normalizing for Input data. Finally, results are compared to results of regression method  

    Real Time Trend Forecasting of Noisy Signal Using Deep Recurrent LSTM Network

    , M.Sc. Thesis Sharif University of Technology Aghaee, Arman (Author) ; Vosoughi Vahdat, Bijan (Supervisor)
    Artificial neural networks are mathematical models inspired by the nervous system and brain. The types and applications of these networks are very widespread nowadays, and it seems that they can be used to track the signals well and estimate the data of the next. In this research, we try to present a model that can predict the future of the trend of noisy signals that have unpredictable behavior, or in other words, chaotic signals. Such research is also widely used in the medical sciences, including the diagnosis of epileptic seizures or heart attacks. In this research, a study with high volatility financial data has been done as an example on this issue and the proposed model tries to be... 

    Stock Price Prediction Based on Shareholders Trading Behavior

    , M.Sc. Thesis Sharif University of Technology Masoud, Mahsa (Author) ; Habibi, Jafar (Supervisor)
    Nowadays, the capital market has a significant impact on the economy of a country and causes economic dynamism and growth in gross production. Among the important phenomena in the stock market is stock pricing, the correctness or incorrectness of which has a significant role in the performance of the stock market and the value of companies. The stock price in the stock exchange represents the stock market value and usually represents the investment value of the shareholders. Forecasting the trend of the stock market is considered an important and necessary thing and has been given much attention, because the successful forecasting of the stock price may lead to attractive profits by making... 

    Prognostics of Rolling Element Bearings and Determining the Condition Monitoring Intervals Using LSTM

    , M.Sc. Thesis Sharif University of Technology Hosseinli, Ali (Author) ; Behzad, Mehdi (Supervisor)
    This study proposes a method to predict the remaining useful life (RUL) of the rolling element bearings (REBs) by forecasting the future trend of the peak of the acceleration signal. It is also employed to determine an appropriate time interval between the measurements of REBs vibration to reduce the error of forecasting and avoid collecting too much data in addition to increasing the reliability. In the first step, in order to achieve better results, the history of the acceleration peak is transformed into a stationary space before using the long short-term memory (LSTM) model to make it normally distributed and stationary. Then, LSTM forecasts the future trend of the stationary time series... 

    International Oil Price Time Series Prediction Using GMDH Neural Network and its Performance Comparison with MLP Neural Network and ARIMA Method

    , M.Sc. Thesis Sharif University of Technology Ghazanfari, Mahdi (Author) ; Haji, Alireza (Supervisor)
    Predicting oil prices, especially in exporting countries, will help governments in the policy-making process by obtaining a reliable estimate of oil revenues. The existence of a complex mechanism governing the process of oil price formation has reduced the efficiency of linear models in forecasting and led researchers to use nonlinear intelligent systems to predict oil prices. In this study, after a detailed study of the structure of artificial neural network, two models of neural network GMDH and MLP and ARIMA method have been used to predict oil price. There are important factors in the prediction process with neural networks, and if all these factors are selected correctly; One can expect... 

    Fault Growth Forecasting of Rotatory Systems Using Wavelet Transform and Artificial Neural Network Algorithm

    , M.Sc. Thesis Sharif University of Technology Sohrabi, Ahmad (Author) ; Behzad, Mahdi (Supervisor) ; Mahdigholi, Hamid (Supervisor)
    Failure of mechanical parts in the industry lead to a larger system downtime and even imposing economic losses to the factory. For this Purpose, for many years, researchers have been trying to find ways to predict early failure and to prevent losses from occurring. Creation of new sciences like artificial intelligence, helped researchers in this field.In the current study, using experimental data of a set of bearings that have been tested and recorded in the Intelligent Systems Research Center, A new approach with sufficient accuracy is presented for the prediction algorithm. Among the features extracted, three features of entropy, root mean square and maximum are the most appropriate... 

    Forecasting Airline Demand by Using Hybric Bayesian Method and Time Series

    , M.Sc. Thesis Sharif University of Technology Shokouhi Seta, Hamid Reza (Author) ; Refie, Majid (Supervisor)
    Using revenue management in any industry can increase the profit. In aviation industries, due to the huge number of requests and travels for each airline, a revenue management system can lead to a good profit for the airlines. The first step in revenue management system is predicting the demand.In this article two models are developed using time series techniques, based on the information taken from one of the Iranian airlines in Tehran-Mashhad fly route.The first model is developed using ARIMA and seasonal-ARIMA models and the second one is based on the demand and price history, price in the day of prediction and the ARIMA model. The second model which is a combination of price, prior price... 

    House Value Forecasting Based on Time Series

    , M.Sc. Thesis Sharif University of Technology Ahmadi, Shahrzad (Author) ; Shavandi, Hassan (Supervisor) ; Khedmati, Majid (Supervisor)
    Making money and maintaining the value of assets has always been one of the most important concerns of people. Real estate is one of the essential human needs, but it is also considered an investment tool for individuals. In addition to individuals in a family, various groups and organizations such as policymakers, analysts, banks and financial institutions, taxpayers, and real estate investors are directly or indirectly affected by the dynamic characteristic of the housing market. Therefore, forecasting the exact amount of housing value in the future is very important. Factors that can improve this forecasting's accuracy include considering the relationship between housing value and... 

    Processing the Local Field Potential Signals in Comparison to Neighboring Simple and Complex Neurons of Primary Visual Cortex

    , M.Sc. Thesis Sharif University of Technology Eftekhar, Morteza (Author) ; Lashgari, Reza (Supervisor)
    In neural systems of living organism, moreover than differences in anatomic structure of cells, there is also differences in physiological functions of analogous cells.Specification and categorization of neurons based on physiological functions is one of objectives of neuroscience. Study of cognitive behaviors and systematic study of neural system, modeling and practical applications in neural prosthesis design are some of applications of categorizing neural cells. Neural signals can be studied by Spike rate of a single neuron activity or Local Field Potential (LFP) of a finite number of neurons. In previous studies neurons of first visual cortex are divided into two groups of simple and... 

    S&P500 Intelligent Trading Using Neural Networks

    , M.Sc. Thesis Sharif University of Technology Hoseinzade, Saeid (Author) ; Akhavan Niaki, Taghi (Supervisor)
    This project tries to select the inputs which really affect the change in the direction of S&P500. For this purpose, design of experiments and analysis of variance are used. T tests are carried out to calculate the statistical significance of mean differences. Experiment results indicate that the designed neural networks with the selected inputs significantly outperform the traditional logit model with respect of the number of correct predictions. Moreover, real trades are simulated using the neural network predictions in the test period and the results show that using the designed neural network can significantly increase the income.


    Study of Statistical Behavior of Chaotic Maps and Design of Stochastic Models for Reconstruction and Prediction of Behavioral Patterns of Chaotic Systems

    , M.Sc. Thesis Sharif University of Technology Jokar, Meysam (Author) ; Salarieh, Hassan (Supervisor) ; Alasty, Aria (Supervisor)
    Chaotic time series analysis, study of statistical behavior of chaotic maps and eventually an attempt to reconstruction and prediction of dynamical and statistical properties of output data of chaotic systems using stochastic models such as Markov models and autoregressive-moving average models are the main purposes of the present research. Examples of chaotic time series abound in the output of economics, engineering systems, the natural sciences (especially geophysics and meteorology) and social sciences. An intrinsic feature of an output time series of a dynamic system is that, adjacent observations are dependent. Time series analysis is concerned with techniques for the analysis of this... 

    Biomolecules and Polymers Translocation Through Biological Single Nanopores and Current Characteristics Analysis

    , Ph.D. Dissertation Sharif University of Technology Haji Abdolvahab, Rouhollah (Author) ; Ejtehadi, Mohammad Reza (Supervisor) ; Mobasheri, Hamid (Co-Advisor)
    Translocation processes are ubiquitous in biology and biotechnology. Translocation of small molecules, e. g. sugar from maltoporin, metabolites through bacteria and macromolecules like proteins, from channels of cellular organelles and or RNA translocation though
    nuclear pores are of vital importance for cellular metabolism. One of the important applications of translocation processes in biotechnology is to sense translocating macromolecules or small molecules by analyzing the current passing through natural or synthesis channels. Improving our knowledge about this process can also help us to develop new methods for designing the appropriate drugs. In this thesis by studying and... 

    Design of Sensory Gove for Recognition of Persian Sign Language

    , M.Sc. Thesis Sharif University of Technology Sarsharzaedh, Mohammad Mahdi (Author) ; Vossoughi, Gholamreza (Supervisor) ; Parnianpour, Mohammad (Supervisor)
    Sign language is recognized by considering the combination of the hand gesture, orientation, location and patterns of hand and arm movement. These complex interactions make the character and word recognition very challenging. In this paper, with the aid of sensory gloves and multiple inertial measurement units (IMUs) we measure the shoulder and elbow joint trajectories and hand gesture to train the discriminant functions to recognize the words intended and represented by sign language. For recognition of hand gesture, the Naive Baysian classifier was used while for Eulerian angles a new time series similarity measures were computed. Different aggregation method was used to integrate temporal... 

    Activation Detection in fMRI Using Nonlinear Time Series Analysis

    , M.Sc. Thesis Sharif University of Technology Taalimi, Ali (Author) ; Fatemizadeh, Emadeddin (Supervisor)
    Functional Magnetic Resonance Imaging (fMRI) is a recently developed neuroimaging technique with capacity to map neural activity with high spatial precision. To locate active brain areas, the method utilizes local blood oxygenation changes which are reflected as small intensity changes in a special type of MR images. The ability to non-invasively map brain functions provides new opportunities to unravel the mysteries and advance the understanding of the human brain, as well as to perform pre-surgical examinations in order to optimize surgical interventions. To obtain these goals the analysis of fMRI is the first condition which should be met. First methods were linear and assumed the... 

    Identifying the Main Factors Affecting Road Accidents in Iran Through Data Mining, Determining the Optimal Solution in Mitigation and Forecasting its Effectiveness Through Arima Models

    , M.Sc. Thesis Sharif University of Technology Karami, Arya (Author) ; Akhavan Niaki, Taghi (Supervisor)
    Road accidents are unfortunate events that cause more thanl16000 deaths each year in Iran. Intercity accidents require a comprehensive plan to reduce casualties because the number of roads users are increasing and the accidents account for nearlyl65% of fatalities. In this study, we first tried to identify the status of Iran through a study of traffic accidents in the world, and then the research and activities carried out in Iran were analyzed to find new and effective solutions. Using the daily fatalities data froml2008 tol2014, and using the new methodology presented in this research based on the Discrete Fourier Transformation (DFT), the Box-Jenkins models and the Secant method, the...