Master Degree / Yüksek Lisans Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/11147/3008
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Master Thesis Trajectory Prediction of Moving Objects by Means of Neural Networks(Izmir Institute of Technology, 1997) Barışık, Hakan; Aytaç, İsmail Sıtkı; Aytaç, İsmail SıtkıEstimating the three-dimensional motion of an object from a sequence of object positions and orientation is of significant importance in variety of applications in control and robotics. For instance, autonomous navigation, manipulation, servo, tracking, planning and surveillance needs prediction of motion parameters. Although "motion estimation" is an old problem (the formulations date back to the beginning of the century), only recently scientists have provided with the tools from nonlinear system estimation theory to solve this problem eural Networks are the ones which have recently been used in many nonlinear dynamic system parameter estimation context. The approximating ability of the neural network is used to identifY the relation between system variables and parameters of a dynamic system. The position, velocity and acceleration of the object are estimated by several neural networks using the II most recent measurements of the object coordinates as input to the system Several neural network topologies with different configurations are introduced and utilized in the solution of the problem. Training schemes for each configuration are given in detail. Simulation results for prediction of motion having different characteristics via different architectures with alternative configurations are presented comparatively.Master Thesis Image Classifcation by Means of Pattern Recognition Techniques(Izmir Institute of Technology, 1997) Güzel, Cumhur; Püskülcü, HalisImage classification plays an important role in many computer vision tasks such as surface inspection, shape determination etc. Various 2-D image classification techniques are investigated, assessed and a computational method to classifY the 2-D X-ray images is developed and evaluated in this study. Various pattern recognition techniques are devised for the solution of the image classification. Those techniques may be divided into mainly two groups. First one, is mathematical and statistical model based, second one, is the artificial neural network based techniques. We have concentrated on artificial neural network techniques. In the experiments, both techniques were applied for the classification of the VUR (vesico ureteral reflux) images, in this study. However, according to the experiments performed on VUR case study, neural network technique was more successful than others, in terms of classifier. A hybrid method is proposed in this study, rather than pure artificial neural network solution. Representation of images is performed via transformation invariant mathematical structure called Fourier Descriptors and these structures are used as input to train the neural network for the classification part.The application is performed as follows: Feature extraction is performed first, then extracted features are used as pattern vectors for training the neural network. Representation of the shapes in X-ray images is performed by using Fourier Descriptors. Usage of Fourier descriptors as a method of representation of the shapes, provides the transformation invariant' (translation, rotation, scaling invariant structure) representation of X-ray images. These new vector representation is fed to the neural network. Backpropagation is used as a training algorithm. After training is finished, system is readyfor questioning. The minimum-mean-distance and nearest neighbor rules are also applied for the pattern vectors generated for the experiments. But the multilayer perceptron trained by backpropagation outperforms both of these statistical classifiers.Master Thesis Prediction of Energy Consumption of Residential Buildings by Artificial Neural Networks and Fuzzy Logic(Izmir Institute of Technology, 2012) Turhan, Cihan; Gökçen Akkurt, GüldenThere are several ways to attempt to forecast building energy consumption. Different techniques, varying from simple regression to dynamic models that are based on physical principles, can be used for simulation. A frequent hypothesis for all these models is that the input variables should be based on realistic data when they are available, otherwise the evaluation of energy consumption might be under or over estimated. The aim of this thesis is to create simple models based on artificial intelligence methods (artificial neural networks and fuzzy logic) as predicting tools and to compare these methods with a building energy performance software (KEP-IYTE ESS). Architectural projects and heat load calculation reports of 148 apartment buildings (5-13 storey) from three municipalities in Ġzmir provide the input data for the models and software. Building energy consumption is modeled as a function of zoning status, heating system type, number of floors, wall overall heat transfer coefficient, glass type, area/volume ratio, existence of insulation, total external surface area, orientation, number of flats, total external surface area/total useful area, total windows area/total external surface area, width/length, total wall area/total useful floor area, total lighting requirement/total useful floor area and total wall area. Four different artificial neural network models and one fuzzy logic model were constructed, trained, tested and the results were compared with the software outcomes. The lowest mean absolute percentage error (MAPE) and mean absolute deviation (MAD) of ANN models appeared to be 4.1% and 6.57, respectively, which shows that ANN can make accurate predictions. On the other hand, fuzzy model gave an 4.86% and 7.59 of MAPE and MAD, respectively, which can be considered as sufficient accuracy.Master Thesis Data Driven Modeling Using Reinforcement Learning in Autonomous Agents(Izmir Institute of Technology, 2003) Karakurt, Murat; Özdemir, SerhanThis research has aspired to build a system which is capable of solving problems by means of its past experience, especially an autonomous agent that can learn from trial and error sequences. To achieve this, connectionist neural network architectures are combined with the reinforcement learning methods. And the credit assignment problem in multi layer perceptron (MLP) architectures is altered. In classical credit assignment problems, actual output of the system and the previously known data in which the system tries to approximate are compared and the discrepancy between them is attempted to be minimized. However, temporal difference credit assignment depends on the temporary successive outputs. By this new method, it is more feasible to find the relation between each event rather than their consequences.Also in this thesis k-means algorithm is modified. Moreover MLP architectures is written in C++ environment, like Backpropagation, Radial Basis Function Networks, Radial Basis Function Link Net, Self-organized neural network, k-means algorithm.And with their combination for the Reinforcement learning, temporal difference learning, and Q-learning architectures were realized, all these algorithms are simulated, and these simulations are created in C++ environment.As a result, reinforcement learning methods used have two main disadvantages during the process of creating autonomous agent. Firstly its training time is too long, and too many input parameters are needed to train the system. Hence it is seen that hardware implementation is not feasible yet. Further research is considered necessary.Master Thesis The Control of a Manipulator Using Cerebellar Model Articulation Controllers(Izmir Institute of Technology, 2003) Darka, Murat; Özdemir, SerhanThe emergence of the theory of artificial neural networks has made it possible to develop neural learning schemes that can be used to obtain alternative solutions to complex problems such as inverse kinematic control for robotic systems. The cerebellar model articulation controller (CMAC) is a neural network topology commonly used in the field of robotic control which was formulated in the 1970s by Albus. In this thesis, CMAC neural networks are analyzed in detail. Optimum network parameters and training techniques are discussed. The relationship between CMAC network parameters and training techniques are presented. An appropriate CMAC network is designed for the inverse kinematic control of a two-link robot manipulator.Master Thesis Artificial Neural Networks and Fuzzy Logic Applications in Modeling the Compressive Strength of Portland Cement(Izmir Institute of Technology, 2004) Can, Sever; Akkurt, SedatPortland cement production is a complex process that involves the effect of several processing parameters on the quality control of 28-day cement compressive strength (CCS). There are some chemical parameters like the C3S, C2S, C3A, C4AF, and SO3 contents in addition to the physical parameters like Blaine (surface area) and particle size distribution. These factors are all effective in producing a single quantity of 28-day CCS. The long duration of 28 day CCS test provided the motivation for research on predictive models. The purpose for these studies was to be able to predict the strength instead of waiting for 28 days for the test to be complete. In this thesis, artificial intelligence (AI) methods like artificial neural networks (ANNs) and fuzzy logic were used in the modeling of the 28-day CCS. The two models were compared for their quality of fit and for the ease of application.Quality control data from a local cement plant were used in the modeling studies. The data were separated randomly into two parts: the first one contained 100 data points to be used in training and the second part had 50 data points to be used in testing stages of the models. In this study, four different AI models were created and tested (3 ANN, 1 fuzzy logic). One of the ANN models (Model A) had 20 input parameters in 20x20x1 architecture with testing average absolute percentage error (AAPE) of 2.24%. The other ANN model (Model B) had four input parameters (SO3, C3S, Blaine and total alkali amount) in 4x4x1 architecture with AAPE of 2.41%. Both of the Model A and the Model B were created in the MatLAB® environment by writinga custom computer code. The last ANN model (Model C) actually refers to 72 differentANN models created in the MatLAB® neural networks toolbox. In order to obtain a model with the lowest error, different learning algorithms, training functions and architectures in combinations were tested. The lowest AAPE among these models appeared to be 2.31%. The fuzzy logic model (Model D) which had four input parameters (SO3, C3S, Blaine and total alkali amount) was created in the MatLAB fuzzy logic toolbox. In order to write the fuzzy rules, the sensitivity analysis of the Model B was utilized. The AAPE of the Model D was 2.69%. The model was compared with the ANN models for its error levels and ease of application. The results indicated that through the application of fuzzy logic algorithm, a more user friendly andmore explicit model than the ANNs could be produced within successfully low error margins.Master Thesis "ann" Artifical Neural Networks and Fuzzy Logic Models for Cooling Load Prediction(Izmir Institute of Technology, 2005) Bozokalfa, Gökhan; Akkurt, SedatIn this thesis Artificial Neural Networks (ANN) and fuzzy logic models of the building energy use predictions were created. Data collected from a Hawaian 42 storey commercial building chiller plant power consumption and independent hourly climate data were obtained from the National Climate Data Center of the USA. These data were used in both ANN and the fuzzy model setting up and testing. The tropical climate data consisted of dry bulb temperature, wet bulb temperature, dew point temperature, relative humidity percentage, wind speed and wind direction.Both input variables and the output variable of the central chiller plant power consumption were fuzzified, and fuzzy membership functions were employed. The Mamdani fuzzy rules (32 rule) in If .Then format with the centre of gravity (COG; centroid) defuzzification were employed. The average percentage error levels in the fuzzy model and the ANN model were end up with 11.6% (R2.0.88) and 10.3% (R2.0.87), respectively. The fuzzy model is successfully presented for predicting chiller plant energy use in tropical climates with small seasonal and daily variations that makes this fuzzy model.Master Thesis Modelling and Control of Bioprocesses by Using Artificial Neural Networks and Hybridmodel(Izmir Institute of Technology, 2006) Genç, Ömer Sinan; Batıgün, Ayşegül; Batıgün, AyşegülThe aim of this study is modeling and control of bioprocesses by using neural networks and hybrid model techniques. To investigate the modeling techniques, ethanol fermentation with Saccharomyces Cerevisiae and recombinant Zymomonas mobilis and finally gluconic acid fermentation with Pseudomonas ovalis processes are chosen.Model equations of these applications are obtained from literature. Numeric solutions are done in Matlab by using ODE solver. For neural network modeling a part of the numerical data is used for training of the network.In hybrid modeling technique, model equations which are obtained from literature are first linearized then to constitute the hybrid model linearized solution results are subtracted from numerical results and obtained values are taken as nonlinear part of the process. This nonlinear part is then solved by neural networks and the results of the neural networks are summed with the linearized solution results. This summation results constitute the hybrid model of the process. Hybrid and neural network models are compared. In some of the applications hybrid model gives slightly better results than the neural network model. But in all of the applications, required training time is much more less for hybrid model techniques. Also, it is observed that hybrid model obeys the physical constraints but neural network model solutions sometimes give meaningless outputs.In control application, a method is demonstrated for optimization of a bioprocess by using hybrid model with neural network structure. To demonstrate the optimization technique, a well known fermentation process is chosen from the literature.Master Thesis Studying Seepage in a Body of Earth-Fill Dam by (artifical Neural Networks) Anns(Izmir Institute of Technology, 2006) Ersayın, Deniz; Tayfur, Gökmen; Tayfur, GökmenDams are structures that are used especially for water storage , energy production, and irrigation. Dams are mainly divided into four parts on the basis of the type and materials of construction as gravity dams, buttress dams, arch dams, and embankment dams. There are two types of embankment dams: earthfill dams and rockfill dams. In this study, seepage through an earthfill dam's body is investigated using an artificial neural network model. Seepage is investigated since seepage both in the dam's body and under the foundation adversely affects dam's stability. This study specifically investigated seepage in dam.s body. The seepage in the dams body follows a phreatic line. In order to understand the degree of seepage, it is necessary to measure the level of phreatic line. This measurement is called as piezometric measurement. Piezometric data sets which are collected from Jeziorsko earthfill dam in Poland were used for training and testing the developed ANN model. Jeziorsko dam is a non-homogeneous earthfill dam built on the impervious foundation. Artificial Neural Networks are one of the artificial intelligence related technologies and have many properties. In this study the water levels on the upstream and downstream sides of the dam were input variables and the water levels in the piezometers were the target outputs in the artificial neural network model. In the line of the purpose of this research, the locus of the seepage path in an earthfill dam is estimated by artificial neural networks. MATLAB 6 neural network toolbox is used for this study.Master Thesis Using Artificial Neural Networks To Predict Issuance Durations of Occupancy Permit Applications(Izmir Institute of Technology, 2011) Kontbay, Setenay; Doğan, Sevgi ZeynepThis study aims to predict the issuance durations of occupancy permit applications using the delay causes defined in the permit process and reveal the most significant causes affecting the performance of the prediction. Artificial Neural Networks (ANN) is used for predicting the issuance durations of occupancy permit applications. The model is constructed to predict the issuance durations of least once rejected applications made to Izmir Konak Municipality during year 2008. Then, sensitivity analysis is carried out to detect the most significant delay causes affecting the issuance duration. Permit data are examined to reveal the delay causes of occupancy permit process. Six inputs are generated from the delay causes and used in ANN model: 1) Number of missing approval letters, 2) Number of missing payment documents, 3) Number of non-conformances of project to codes and regulations, 4) Number of all missing documents, 5) First permit application season, 6) First permit rejection season. Total issuance durations of the occupancy permit applications are used as the output parameters of the model. The results of the analysis indicate that the prediction accuracy of the model is 86% and the number of missing approval letters, the number of missing payment documents, and the first application season are respectively the three most significant inputs affecting the prediction performance of the model. This study proves that the total issuance durations are so bound to the delay causes in the permit process that it can be learned and predicted by the ANN model and the occupancy permit process is required to be reengineered.