Дисертації з теми "Artificial Neural Network-based modeling"
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Brunger, Clifford A. "Artificial neural network modeling of damaged aircraft." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1994. http://handle.dtic.mil/100.2/ADA283227.
Повний текст джерелаSaptoro, Agus. "An integrated approach to artificial neural network based process modelling." Thesis, Curtin University, 2010. http://hdl.handle.net/20.500.11937/2484.
Повний текст джерелаAjayi, Toluwaleke. "Modeling Discharge and Water Chemistry Using Artificial Neural Network." Ohio University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1620167556121952.
Повний текст джерелаRothrock, Ling. "Modeling skilled decision-making using artificial neural network and genetic-based machine learning techniques." Thesis, Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/25084.
Повний текст джерелаByrne, Brian James. "An evaluation of artificial neural network modeling for manpower analysis." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1993. http://handle.dtic.mil/100.2/ADA273001.
Повний текст джерелаThesis advisor(s): George W. Thomas ; Timothy P. Hill. "September 1993." Includes bibliographical references. Also available online,
FALCIONELLI, NICOLA. "From Symbolic Artificial Intelligence to Neural Networks Universality with Event-based Modeling." Doctoral thesis, Università Politecnica delle Marche, 2020. http://hdl.handle.net/11566/274620.
Повний текст джерелаRepresenting knowledge, modeling human reasoning, and understanding thought processes have always been central parts of intellectual activities, since the first attempts by greek philosophers. It is not just by chance that, as soon as computers started to spread, remarkable scientists and mathematicians such as John McCarthy, Marvin Minsky and Claude Shannon started creating Artificially Intelligent systems with a symbolic oriented perspective. Even though this has been a partially forced path due to the very limited computing capabilities at the time, it marked the beginning of what is now known as Classical (or Symbolic) Artificial Intelligence, or essentially, a set of techniques for implementing "intelligent" behaviours by means of logic formalisms and theorem proving. Classical AI techniques are indeed very direct and human-centered processes, which find their strenghts on straightforward human interpretability and knowledge reusability. On the contrary, they suffer of computability problems when applied to real world tasks, mostly due to search space combinatorial explosion (especially when reasoning with time), and undecidability. However, the ever-increasing capabilites of computer hardware opened new possibilities for other more statistical-oriented methods to grow, such as Neural Networks. Even if the theory behind these methods was long known, it was only in recent years that they managed to achieve significant breakthroughs, and to surpass Classical AI techniques on many tasks. At the moment, the main hurdles of such statistical AI techniques are represented by the high energy consumption and the lack of easy ways for humans to understand the process that led to a particular result. Summing up, Classical and Statistical AI techniques can be seen as two faces of the same coin: if a domain presents structured information, little uncertainty, and clear decision processes, then Classical AI might be the right tool, or otherwise, when the information is less structured, has more uncertainty, ambiguity and clear decision processes cannot be identified, then Statistical AI should be chosen. The main purpose of this thesis is thus (i) to show capabilities and limits of current (Classical and Statistical) Artificial Intelligence techniques in both structured and unstructured domains, and (ii) to demostrate how event-based modeling can tackle some of their critical issues, providing new potential connections and novel perspectives.
FLECK, JULIA LIMA. "ARTIFICIAL NEURAL NETWORK MODELING FOR QUALITY INFERENCE OF A POLYMERIZATION PROCESS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2008. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=12980@1.
Повний текст джерелаO presente trabalho apresenta o desenvolvimento de um modelo neural para a inferência da qualidade do polietileno de baixa densidade (PEBD) a partir dos valores das variáveis de processo do sistema reacional. Para tal, fez- se uso de dados operacionais de uma empresa petroquímica, cujo pré-processamento incluiu a seleção de variáveis, limpeza e normalização dos dados selecionados e preparação dos padrões. A capacidade de inferência do modelo neural desenvolvido neste estudo foi comparada com a de dois modelos fenomenológicos existentes. Para tal, utilizou-se como medida de desempenho o valor do erro médio absoluto percentual dos modelos, tendo como referência valores experimentais do índice de fluidez. Neste contexto, o modelo neural apresentou-se como uma eficiente ferramenta de modelagem da qualidade do sistema reacional de produção do PEBD.
This work comprises the development of a neural network- based model for quality inference of low density polyethylene (LDPE). Plant data corresponding to the process variables of a petrochemical company`s LDPE reactor were used for model development. The data were preprocessed in the following manner: first, the most relevant process variables were selected, then data were conditioned and normalized. The neural network- based model was able to accurately predict the value of the polymer melt index as a function of the process variables. This model`s performance was compared with that of two mechanistic models developed from first principles. The comparison was made through the models` mean absolute percentage error, which was calculated with respect to experimental values of the melt index. The results obtained confirm the neural network model`s ability to infer values of quality-related measurements of the LDPE reactor.
Li, Tan. "Tire-Pavement Interaction Noise (TPIN) Modeling Using Artificial Neural Network (ANN)." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/87417.
Повний текст джерелаPHD
Bhanot, Nishant. "Artificial Neural Networks based Modeling and Analysis of Semi-Active Damper System." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78295.
Повний текст джерелаMaster of Science
Kvale, David Thomas. "Artificial Neural Network-Based Approaches for Modeling the Radiated Emissions from Printed Circuit Board Structures and Shields." University of Toledo / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1280698960.
Повний текст джерелаJoy, Karen Lynn. "Evaluating input variable effects of an artificial neural network modeling facial attractiveness /." Also available to VCU users online at:, 2005. http://hdl.handle.net/10156/1253.
Повний текст джерелаJoy, Karen. "Evaluating Input Variable Effects of an Artificial Neural Network Modeling Facial Attractiveness." VCU Scholars Compass, 2005. http://scholarscompass.vcu.edu/etd_retro/128.
Повний текст джерелаPerez, Miguel A. "Prediction of Whole-body Lifting Kinematics using Artificial Neural Networks." Diss., Virginia Tech, 2005. http://hdl.handle.net/10919/28706.
Повний текст джерелаPh. D.
Akkala, Arjun. "Development of Artificial Neural Networks Based Interpolation Techniques for the Modeling and Estimation of Radon Concentrations in Ohio." University of Toledo / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1279315482.
Повний текст джерелаPagliarini, Silvia. "Modeling the neural network responsible for song learning." Thesis, Bordeaux, 2021. http://www.theses.fr/2021BORD0107.
Повний текст джерелаDuring the first period of their life, babies and juvenile birds show comparable phases of vocal development: first, they listen to their parents/tutors in order to build a neural representation of the experienced auditory stimulus, then they start to produce sound and progressively get closer to reproducing their tutor song. This phase of learning is called the sensorimotor phase and is characterized by the presence of babbling, in babies, and subsong, in birds. It ends when the song crystallizes and becomes similar to the one produced by the adults.It is possible to find analogies between brain pathways responsible for sensorimotor learning in humans and birds: a vocal production pathway involves direct projections from auditory areas to motor neurons, and a vocal learning pathway is responsible for imitation and plasticity. The behavioral studies and the neuroanatomical structure of the vocal control circuit in humans and birds provide the basis for bio-inspired models of vocal learning.In particular, birds have brain circuits exclusively dedicated to song learning, making them an ideal model for exploring the representation of vocal learning by imitation of tutors.This thesis aims to build a vocal learning model underlying song learning in birds. An extensive review of the existing literature is discussed in the thesis: many previous studies have attempted to implement imitative learning in computational models and share a common structure. These learning architectures include the learning mechanisms and, eventually, exploration and evaluation strategies. A motor control function enables sound production and sensory response models either how sound is perceived or how it shapes the reward. The inputs and outputs of these functions lie (1)~in the motor space (motor parameters’ space), (2)~in the sensory space (real sounds) and (3)~either in the perceptual space (a low dimensional representation of the sound) or in the internal representation of goals (a non-perceptual representation of the target sound).The first model proposed in this thesis is a theoretical inverse model based on a simplified vocal learning model where the sensory space coincides with the motor space (i.e., there is no sound production). Such a simplification allows us to investigate how to introduce biological assumptions (e.g. non-linearity response) into a vocal learning model and which parameters influence the computational power of the model the most. The influence of the sharpness of auditory selectivity and the motor dimension are discussed.To have a complete model (which is able to perceive and produce sound), we needed a motor control function capable of reproducing sounds similar to real data (e.g. recordings of adult canaries). We analyzed the capability of WaveGAN (a Generative Adversarial Network) to provide a generator model able to produce realistic canary songs. In this generator model, the input space becomes the latent space after training and allows the representation of a high-dimensional dataset in a lower-dimensional manifold. We obtained realistic canary sounds using only three dimensions for the latent space. Among other results, quantitative and qualitative analyses demonstrate the interpolation abilities of the model, which suggests that the generator model we studied can be used as a motor function in a vocal learning model.The second version of the sensorimotor model is a complete vocal learning model with a full action-perception loop (i.e., it includes motor space, sensory space, and perceptual space). The sound production is performed by the GAN generator previously obtained. A recurrent neural network classifying syllables serves as the perceptual sensory response. Similar to the first model, the mapping between the perceptual space and the motor space is learned via an inverse model. Preliminary results show the influence of the learning rate when different sensory response functions are implemented
Moral, Hakan. "Modeling Of Activated Sludge Process By Using Artificial Neural Networks." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12605733/index.pdf.
Повний текст джерелаDenitrification (SSSP) program, which is an implementation of Activated Sludge Model No 1 (ASM1), was used as the source of input and output data. The other systems were actual treatment plants, Ankara Central Wastewater Treatment Plant, ACWTP and iskenderun Wastewater Treatment Plant (IskWTP). A sensitivity analysis was applied for the hypothetical plant for both of the model simulation results obtained by the SSSP program and the developed ANN model. Sensitivity tests carried out by comparing the responses of the two models indicated parallel sensitivities. In hypothetical WWTP modeling, the highest correlation coefficient obtained with ANN model versus SSSP was about 0.980. By using actual data from IskWTP the best fit obtained by the ANN model yielded R value of 0.795 can be considered very high with such a noisy data. Similarly, ACWTP the R value obtained was 0.688, where accuracy of fit is debatable.
Cretu, Ana-Maria. "Neural network modeling of three-dimensional objects for virtualized reality applications." Thesis, University of Ottawa (Canada), 2003. http://hdl.handle.net/10393/26465.
Повний текст джерелаKhodaverdi, Afaghi Mahtab. "Application of artificial neural network modeling in thermal process calculations of canned foods." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape2/PQDD_0033/MQ64381.pdf.
Повний текст джерелаBajracharya, Dinesh. "Econometric Modeling vs Artificial Neural Networks : A Sales Forecasting Comparison." Thesis, Högskolan i Borås, Institutionen Handels- och IT-högskolan, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-20400.
Повний текст джерелаProgram: Magisterutbildning i informatik
ABU, OMAR OSAMA YOUSEF. "ARTIFICIAL NEURAL NETWORK MODELING OF FLOW STRESS RESPONSE AS A FUNCTION OF DISLOCATION MICROSTRUCTURES." MSSTATE, 2007. http://sun.library.msstate.edu/ETD-db/theses/available/etd-06222007-112519/.
Повний текст джерелаKessler, Brian Scott. "Development of an integrated approach combining artificial neural network material based on modeling with finite element analysis of forming processes." Diss., Columbia, Mo. : University of Missouri-Columbia, 2005. http://hdl.handle.net/10355/4164.
Повний текст джерелаThe entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (May 24, 2006) Vita. Includes bibliographical references.
Yang, Zhiguo. "MODELING LARGE-SCALE CROSS EFFECT IN CO-PURCHASE INCIDENCE: COMPARING ARTIFICIAL NEURAL NETWORK TECHNIQUES AND MULTIVARIATE PROBIT MODELING." UKnowledge, 2015. http://uknowledge.uky.edu/busadmin_etds/6.
Повний текст джерелаFang, Zhufeng. "USING GEOSTATISTICS, PEDOTRANSFER FUNCTIONS TO GENERATE 3D SOIL AND HYDRAULIC PROPERTY DISTRIBUTIONS FOR DEEP VADOSE ZONE FLOW SIMULATIONS." Thesis, The University of Arizona, 2009. http://hdl.handle.net/10150/193439.
Повний текст джерелаThekkudan, Travis Francis. "Calibration of an Artificial Neural Network for Predicting Development in Montgomery County, Virginia: 1992-2001." Thesis, Virginia Tech, 2008. http://hdl.handle.net/10919/33732.
Повний текст джерелаMaster of Science
Mudunuru, Venkateswara Rao. "Modeling and Survival Analysis of Breast Cancer: A Statistical, Artificial Neural Network, and Decision Tree Approach." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6120.
Повний текст джерелаThai, Shee Meng. "Neural network modelling and control of coal fired boiler plant." Thesis, University of South Wales, 2005. https://pure.southwales.ac.uk/en/studentthesis/neural-network-modelling-and-control-of-coal-fired-boiler-plant(b5562ca0-e45e-44d8-aad2-ed2e3e114808).html.
Повний текст джерелаVACCA, GIANMARCO. "Redundancy Analysis Models with Categorical Endogenous Variables: New Estimation Techniques Based on Vector GLM and Artificial Neural Networks." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2017. http://hdl.handle.net/10281/158304.
Повний текст джерелаStructural Equation Models with latent variables have considerably developed in recent years. Starting from the pioneers of the two most prominent ways of defining models with latent variables, namely Covariance Structure Analysis and Component Analysis, with LISREL and PLS-PM as the most famous techniques, several extensions and improvements have been put forward. Moreover, for Redundancy Analysis models, which are part of the Component Analysis framework, but have only observed endogenous variables, new methods have been proposed in literature to deal with more than one group of exogenous observed variables, with simple linear equations and a unified optimization problem. One main criticism, that has been dealt with recently in new strands of literature regarding Structural Equation Modeling, is the partial inability of these systems of linear equations to deal with categorical indicators. Several methods have been proposed, in PLS-PM and LISREL respectively, either related to Optimal Scaling, or adapting the EM algorithm to the particular case under examination. In the Redundancy Analysis framework, with only observed endogenous variables, the possibility of extending the estimation procedures to a qualitative setting is considerably less hampered by model restrictions, even more so in the Extended Redundancy Analysis model, with more than one block of exogenous variables. This work will hence present a new estimation of Extended Redundancy Analysis models in presence of binary or categorical endogenous variables, with two main estimation techniques: Iterated Reweighed Least Squares, and Gradient Descent with backpropagation in an Artificial Neural Network architecture. For the latter, recent developments in Structural Equation Models in the neural networks setting will be firstly examined, and the new technique will be subsequently introduced.
Li, Xin. "Abstractive Representation Modeling for Image Classification." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1623250959448677.
Повний текст джерелаZhao, Yi. "Combination of Wireless sensor network and artifical neuronal network : a new approach of modeling." Thesis, Toulon, 2013. http://www.theses.fr/2013TOUL0013/document.
Повний текст джерелаA Wireless Sensor Network (WSN) consisting of autonomous sensor nodes can provide a rich stream of sensor data representing physical measurements. A well built Artificial Neural Network (ANN) model needs sufficient training data sources. Facing the limitation of traditional parametric modeling, this paper proposes a standard procedure of combining ANN and WSN sensor data in modeling. Experiments on indoor thermal modeling demonstrated that WSN together with ANN can lead to accurate fine grained indoor thermal models. A new training method "Multi-Pattern Cross Training" (MPCT) is also introduced in this work. This training method makes it possible to merge knowledge from different independent training data sources (patterns) into a single ANN model. Further experiments demonstrated that models trained by MPCT method shew better generalization performance and lower prediction errors in tests using different data sets. Also the MPCT based Neural Network Model has shown advantages in multi-variable Neural Network based Model Predictive Control (NNMPC). Software simulation and application results indicate that MPCT implemented NNMPC outperformed Multiple models based NNMPC in online control efficiency
Narayanan, Pavanesh. "Sensor-less Control of Shape Memory Alloy Using Artificial Neural Network and Variable Structure Controller." University of Toledo / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1416501021.
Повний текст джерелаBataineh, Mohammad Hindi. "New neural network for real-time human dynamic motion prediction." Thesis, The University of Iowa, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3711174.
Повний текст джерелаArtificial neural networks (ANNs) have been used successfully in various practical problems. Though extensive improvements on different types of ANNs have been made to improve their performance, each ANN design still experiences its own limitations. The existing digital human models are mature enough to provide accurate and useful results for different tasks and scenarios under various conditions. There is, however, a critical need for these models to run in real time, especially those with large-scale problems like motion prediction which can be computationally demanding. For even small changes to the task conditions, the motion simulation needs to run for a relatively long time (minutes to tens of minutes). Thus, there can be a limited number of training cases due to the computational time and cost associated with collecting training data. In addition, the motion problem is relatively large with respect to the number of outputs, where there are hundreds of outputs (between 500-700 outputs) to predict for a single problem. Therefore, the aforementioned necessities in motion problems lead to the use of tools like the ANN in this work.
This work introduces new algorithms for the design of the radial-basis network (RBN) for problems with minimal available training data. The new RBN design incorporates new training stages with approaches to facilitate proper setting of necessary network parameters. The use of training algorithms with minimal heuristics allows the new RBN design to produce results with quality that none of the competing methods have achieved. The new RBN design, called Opt_RBN, is tested on experimental and practical problems, and the results outperform those produced from standard regression and ANN models. In general, the Opt_RBN shows stable and robust performance for a given set of training cases.
When the Opt_RBN is applied on the large-scale motion prediction application, the network experiences a CPU memory issue when performing the optimization step in the training process. Therefore, new algorithms are introduced to modify some steps of the new Opt_RBN training process to address the memory issue. The modified steps should only be used for large-scale applications similar to the motion problem. The new RBN design proposes an ANN that is capable of improved learning without needing more training data. Although the new design is driven by its use with motion prediction problems, the consequent ANN design can be used with a broad range of large-scale problems in various engineering and industrial fields that experience delay issues when running computational tools that require a massive number of procedures and a great deal of CPU memory.
The results of evaluating the modified Opt_RBN design on two motion problems are promising, with relatively small errors obtained when predicting approximately 500-700 outputs. In addition, new methods for constraint implementation within the new RBN design are introduced. Moreover, the new RBN design and its associated parameters are used as a tool for simulated task analysis. This work initiates the idea that output weights (W) can be used to determine the most critical basis functions that cause the greatest reduction in the network test error. Then, the critical basis functions can specify the most significant training cases that are responsible for the proper performance achieved by the network. The inputs with the most change in value can be extracted from the basis function centers (U) in order to determine the dominant inputs. The outputs with the most change in value and their corresponding key body degrees-of-freedom for a motion task can also be specified using the training cases that are used to create the network's basis functions.
CHANG, DYI-HUEY. "ANALYSIS AND MODELING OF SPACE-TIME ORGANIZATION OF REMOTELY SENSED SOIL MOISTURE." University of Cincinnati / OhioLINK, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1011125319.
Повний текст джерелаFletcher, Eric Matthew. "FE-ANN based modeling of 3D simple reinforced concrete girders for objective structural health evaluation." Thesis, Kansas State University, 2016. http://hdl.handle.net/2097/32497.
Повний текст джерелаDepartment of Civil Engineering
Hayder A. Rasheed
The structural deterioration of aging infrastructure systems is becoming an increasingly important issue worldwide. To compound the issue, economic strains limit the resources available for repair or replacement of such systems. Over the past several decades, structural health monitoring (SHM) has proved to be a cost-effective method for detection and evaluation of damage in structures. Visual inspection and condition rating is one of the most commonly applied SHM techniques, but the effectiveness of this method suffers due to its reliance on the availability and experience of qualified personnel performing largely qualitative damage evaluations. The artificial neural network (ANN) approach presented in this study attempts to augment visual inspection methods by developing a crack-induced damage quantification model for reinforced concrete bridge girders that requires only the results of limited field measurements to operate. Simply-supported three-dimensional reinforced concrete T-beams with varying geometric, material, and cracking properties were modeled using Abaqus finite element (FE) analysis software. Up to five cracks were considered in each beam, and the ratios of stiffness between cracked and healthy beams with the same geometric and material parameters were measured at nine equidistant nodes along the beam. Two feedforward ANNs utilizing backpropagation learning algorithms were then trained on the FE model database with beam properties serving as inputs for both neural networks. The outputs for the first network consisted of the nodal stiffness ratios, and the sole output for the second ANN was a health index parameter, computed by normalizing the area under the stiffness ratio profile over the span length of the beam. The ANNs achieved excellent prediction accuracies with coefficients of determination (R²) exceeding 0.99 for both networks. Additional FE models were created to further assess the networks’ prediction capabilities on data not utilized in the training process. The ANNs displayed good prediction accuracies (R² > 0.8) even when predicting damage levels in beams with geometric, material, and cracking parameters dissimilar from those found in the training database. A touch-enabled user interface was developed to allow the ANN models to be utilized for on-site damage evaluations. The results of this study indicate that application of ANNs with FE modeling shows great promise in SHM for damage evaluation.
Sen, Baris Ali. "Artificial neural networks based subgrid chemistry model for turbulent reactive flow simulations." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31757.
Повний текст джерелаCommittee Chair: Menon, Suresh; Committee Member: Lieuwen, Timothy C.; Committee Member: Sankar, Lakshmi; Committee Member: Stoesser, Thorsten; Committee Member: Syed, Saadat; Committee Member: Walker, Mitchell. Part of the SMARTech Electronic Thesis and Dissertation Collection.
Rosquist, Parker Gary. "Modeling Three Dimensional Ground Reaction Force Using Nanocomposite Piezoresponsive Foam Sensors." BYU ScholarsArchive, 2017. https://scholarsarchive.byu.edu/etd/6390.
Повний текст джерелаRamalho, José Pinto. "Oxicorte: estudo da transferência de calor e modelamento por redes neurais artificiais de variáveis do processo." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/3/3133/tde-30092008-150619/.
Повний текст джерелаOxygen cutting process produces surfaces that vary from a machine cut finishing to one of virtually no quality at all. Besides equipment conditions and operators\' skills, these possibilities result from the correct selection of work parameters and variables. The process is based on a highly exothermic chemical reaction that generates part of the heat needed for its occurrence, along with the rest of heat resultant from the flame of the blowpipe. The ratio between these values depends highly on the thickness of the material used. This work shows how to calculate the amount of energy generated in the cutting process. Based on two methodologies of different authors, this research studies how factors such as the change in the oxygen concentration and the pre heating temperature of plates can vary the heat balance and simulates, with the use of Artificial Neural Networks, some of the data needed to perform these calculations. ASTM A36 carbon steel plates, from 12.7 to 50.8 mm thick, with different oxygen concentration (99,5% e 99,95%) and preheating temperatures (30 and 230 ±30ºC) were cut. The cut surfaces and the produced oxides were characterized and the results were correlated with the use of mathematical treatment and artificial intelligence techniques. In order to carry out this work some previously inexistent aspects in literature have been developed, such as a Fe oxides characterization methodology with X-ray diffraction and Rietveld method; the use of artificial neural networks to simulate the results in the oxygen cutting process and the comparison between different artificial neural networks, which are unpublished aspects of this work that can be seen in seven technical papers published while this work was in progress. Results show: a methodology for the analysis of the energy efficiency of the process; the development of techniques that, together with artificial intelligence, simulate the results of aspects of the process; which finally allows the simulation analysis of the energy efficiency of the process.
Sarlak, Nermin. "Evaluation And Modeling Of Streamflow Data: Entropy Method, Autoregressive Models With Asymmetric Innovations And Artificial Neural Networks." Phd thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/3/12606135/index.pdf.
Повний текст джерелаTavares, Guilherme Farias. "Modelagem matemática e sistemas inteligentes para predição do comportamento alimentar de suínos nas fases de crescimento e terminação." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/11/11152/tde-28072017-082242/.
Повний текст джерелаThe swine production in an activity of great importance to Brazil and to the world. However, because they maintain a constant body temperature and, alterations in the thermic accommodation environment can directly affect their physiological and behavioral responses for maintaining the internal temperature. Thus, the objective of this study was to access the feeding behavior of growing-finishing pigs of different sirelines and gender and its relationship with climate variables (thermic environment). Furthermore, mathematical models based on classic logic was developed as well as an intelligent system for predicting the total time spent eating (TM, min day -1). The data was collected in an experimental farm located in Clay Center, Nebraska, United States. The experimental period contemplated two seasons (summer and winter), totalizing 63 days (9 weeks) of information collected for each season. The housed animals were from three different commercial sirelines: Landrace, Duroc and Yorkshire. Each pen presented a mix composition, being housed 40 animals of different sirelines and gender. In total, there were 240 housed animals, being 80 animals for each sireline among barrows and gilts. The data registered were air temperature (Tar, °C), dew point temperature (Tpo, °C) and relative humidity of the air (UR, %) every 5 minutes inside the facility. For TM, the data were collected and registered every 20 seconds by a radio frequency data collection system. The thermal comfort was analyzed from the Temperature and Humidity Index (THI) and Specific Enthalpy (H, kJ kg-1 of dry air). In order to evaluate the relationship between the thermic environment and TM, the multivariate statistics through principal component analysis (PCA) and grouping was utilized for obtaining the selection standards of variables to enter in the models. The fuzzy model and the artificial neural networks were developed in a MATLAB® R2015a environment through the Fuzzy and the Neural Network toolboxes with the objective to predict TM, having as entry variables: sireline, gender, age and THI. On the whole, the Tar averages were inside the thermoneutral zone (ZCT), however, these values were below the superior critic THI. In the face of the results analysis, it could be observed in ration to the feeding behavior that the Landrace gilt presented the shortest time eating with averages of 42.19 min day-1 and 43.73 min day-1 for winter and summer respectively followed by the barrow from the same sireline, while the other sirelines presented values above 60 min day-1. It was not observed a significative linear correlation between the thermic environment and TM once the animals were inside their ZCT throughout all the experimentation period, indicating that the feeding behavior was influenced mainly by the homeostatic and cognitivehedonic factors. The multivariate statistics divided the animals in 8 groups, being observed that animals of different sirelines and gender behave the same way throughout the experimentation period, making the mathematical modeling difficult. However, some groups presented a bigger amount of animals of determined sireline and gender, being utilized as \"standard groups\" for the development of the fuzzy model and the artificial neural network. The fuzzy model presented an R2 of 0,858 when utilizing the \"standard group\" data, however, for all the values the R2 was 0.549. In the other hand the neural network presented an R2 of 0.611 for the complete data and an R2 of 0.914 for the \"standard group\". Thus, the artificial neural network appeared to be a tool of a better precision and accuracy when predicting the feeding behavior of pigs on growing-finishing phases.
Aslan, Muhittin. "Modeling The Water Quality Of Lake Eymir Using Artificial Neural Networks (ann) And Adaptive Neuro Fuzzy Inference System (anfis)." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12610211/index.pdf.
Повний текст джерелаMatlab R 2007b&rdquo
software was used. The results indicated that ANN has high prediction capacity of DO and ANFIS has low with respect to ANN. Failure of ANFIS was due to low functionality of Matlab ANFIS Graphical User Interface. For ANN Modeling effect of meteorological data on DO data on surface of the lake was successfully described and summer month super saturation DO concentrations were successfully predicted.
Grose, Mitchell. "Forecasting Atmospheric Turbulence Conditions From Prior Environmental Parameters Using Artificial Neural Networks: An Ensemble Study." University of Dayton / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1619632748733788.
Повний текст джерелаMaragno, Donato. "Optimization with machine learning-based modeling: an application to humanitarian food aid." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21621/.
Повний текст джерелаScarborough, David J. (David James). "An Evaluation of Backpropagation Neural Network Modeling as an Alternative Methodology for Criterion Validation of Employee Selection Testing." Thesis, University of North Texas, 1995. https://digital.library.unt.edu/ark:/67531/metadc277752/.
Повний текст джерелаHopkins, Brad Michael. "A Wavelet-Based Rail Surface Defect Prediction and Detection Algorithm." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/77351.
Повний текст джерелаPh. D.
Källman, Jonas. "Ship Power Estimation for Marine Vessels Based on System Identification." Thesis, Linköpings universitet, Reglerteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-79248.
Повний текст джерелаUtai, Katrin [Verfasser]. "Using real-time image processing and active thermography with artificial neural network modeling for non-destructive mango quality assessment / Katrin Utai." Aachen : Shaker, 2018. http://d-nb.info/118854926X/34.
Повний текст джерелаAl-Shayji, Khawla Abdul Mohsen. "Modeling, Simulation, and Optimization of large-Scale Commercial Desalination Plants." Diss., Virginia Tech, 1998. http://hdl.handle.net/10919/30462.
Повний текст джерелаPh. D.
More, Priyanka Ramesh. "Using Machine Learning to predict water table levels in a wet prairie in Northwest Ohio." Bowling Green State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1535334208410497.
Повний текст джерелаAragao, Almeida Salvio Jr. "Modeling of Concrete Anchors Supporting Non-Structural Components Subjected toStrong Wind and Adverse Environmental Conditions." University of Toledo / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1564764404011142.
Повний текст джерелаBrown, Michael Kenneth. "Landslide Detection and Susceptibility Mapping Using LiDAR and Artificial Neural Network Modeling: A Case Study in Glacially Dominated Cuyahoga River Valley, Ohio." Bowling Green State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1350307168.
Повний текст джерелаGummadi, Jayaram. "A Comparison of Various Interpolation Techniques for Modeling and Estimation of Radon Concentrations in Ohio." University of Toledo / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1376567646.
Повний текст джерела