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Relevant bibliographies by topics / Feed Forward Neural Network (FFNN) / Dissertations / Theses

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Dissertations / Theses on the topic 'Feed Forward Neural Network (FFNN)'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 February 2022

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1

Khanna, Neha, and Neha Khanna@mdbc gov au. "Investigation of phytoplankton dynamics using time-series analysis of biophysical parameters in Gippsland Lakes, South-eastern Australia." RMIT University. Civil, Environmental and Chemical Engineering, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080226.123435.

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There is a need for ecological modelling to help understand the dynamics in ecological systems, and thus aid management decisions to maintain or improve the quality of the ecological systems. This research focuses on non linear statistical modelling of observations from an estuarine system, Gippsland Lakes, on the south-eastern coast of Australia. Feed forward neural networks are used to model chlorophyll time series from a fixed monitoring station at Point King. The research proposes a systematic approach to modelling in ecology using feed forward neural networks, to ensure: (a) that res

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Hadjiprocopis, Andreas. "Feed forward neural network entities." Thesis, City University London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340374.

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3

Tanaka, Toshiyuki. "Control of growth dynamics of feed-forward neural network." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/13445.

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Al-Mudhaf, Ali F. "A feed forward neural network approach for matrix computations." Thesis, Brunel University, 2001. http://bura.brunel.ac.uk/handle/2438/5010.

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A new neural network approach for performing matrix computations is presented. The idea of this approach is to construct a feed-forward neural network (FNN) and then train it by matching a desired set of patterns. The solution of the problem is the converged weight of the FNN. Accordingly, unlike the conventional FNN research that concentrates on external properties (mappings) of the networks, this study concentrates on the internal properties (weights) of the network. The present network is linear and its weights are usually strongly constrained; hence, complicated overlapped network needs to

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5

Richards, Gareth D. "Implementation and capabilities of layered feed-forward networks." Thesis, University of Edinburgh, 1990. http://hdl.handle.net/1842/11313.

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Mohammadi, Mohammad Mehdi. "PREDICTION OF WIND TURBINE BLADE FATIGUE LOADS USING FEED-FORWARD NEURAL NETWORKS." Thesis, Uppsala universitet, Institutionen för geovetenskaper, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-444115.

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In recent years, machine learning applications have gained great attention in the wind power industry. Among these, artificial neural networks have been utilized to predict the fatigue loads of wind turbine components such as rotor blades. However, the limited number of contributions and differences in the used databases give rise to several questions which this study has aimed to answer. Therefore, in this study, 5-min SCADA data from the Lillgrund wind farm has been used to train two feed-forward neural networks to predict the fatigue loads at the blade root in flapwise and edgewise directio

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Nyman, Jacob. "Machinery Health Indicator Construction using Multi-objective Genetic Algorithm Optimization of a Feed-forward Neural Network based on Distance." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-298084.

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Assessment of machine health and prediction of future failures are critical for maintenance decisions. Many of the existing methods use unsupervised techniques to construct health indicators by measuring the disparity between the current state and either the healthy or the faulty states of the system. This approach can work well, but if the resulting health indicators are insufficient there is no easy way to steer the algorithm towards better ones. In this thesis a new method for health indicator construction is investigated that aims to solve this issue. It is based on measuring distance afte

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8

Nigrini, L. B., and G. D. Jordaan. "Short term load forecasting using neural networks." Journal for New Generation Sciences, Vol 11, Issue 3: Central University of Technology, Free State, Bloemfontein, 2013. http://hdl.handle.net/11462/646.

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Published Article<br>Several forecasting models are available for research in predicting the shape of electric load curves. The development of Artificial Intelligence (AI), especially Artificial Neural Networks (ANN), can be applied to model short term load forecasting. Because of their input-output mapping ability, ANN's are well-suited for load forecasting applications. ANN's have been used extensively as time series predictors; these can include feed-forward networks that make use of a sliding window over the input data sequence. Using a combination of a time series and a neural network pr

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9

Karlsson, Nils. "Comparison of linear regression and neural networks for stock price prediction." Thesis, Uppsala universitet, Signaler och system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-445237.

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Stock market prediction has been a hot topic lately due to advances in computer technology and economics. One economic theory, called Efficient Market Hypothesis (EMH), states that all known information is already factored into the prices which makes it impossible to predict the stock market. Despite the EMH, many researchers have been successful in predicting the stock market using neural networks on historical data. This thesis investigates stock prediction using both linear regression and neural networks (NN), with a twist. The inputs to the proposed methods are a number of profit predictio

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Gróf, Zoltán. "Realizace rozdělujících nadploch." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219781.

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The main aim of this master's thesis is to describe the subject of the implementation of decision boundaries with the help of artificial neural networks. The objective is to present theoretical knowledge concerning this field and on practical examples prove these statements. The work contains basic theoretical description of the field of pattern recognition and the field of feature based representation of objects. A classificator working on the basis of Bayes decision is presented in this part, and other types of classificators are named as well. The work then deals with artificial neural netw

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Prosperi, Maurizio. "Analysis of the EU MacSharry reform in Italian family farms : a multilayer feed-forward neural network model for evaluating economic and environmental impacts of the direct payments system." Kyoto University, 2005. http://hdl.handle.net/2433/144591.

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Kyoto University (京都大学)<br>0048<br>新制・課程博士<br>博士(農学)<br>甲第11837号<br>農博第1527号<br>新制||農||919(附属図書館)<br>学位論文||H17||N4086(農学部図書室)<br>23597<br>UT51-2005-K503<br>京都大学大学院農学研究科生物資源経済学専攻<br>(主査)教授加賀爪優, 教授吉田昌之, 教授小田滋晃<br>学位規則第4条第1項該当

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Negrini, Melissa. "Tatto artificiale: studio ed implementazione di una rete neurale per la localizzazione di impatti." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/17342/.

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Il tatto artificiale è un elaborato che tratta il sistema nervoso artificiale a partire dalla descrizione del sistema nervoso biologico. Nei primi capitoli viene descritto nel particolare il sistema nervoso biologico e di seguito il sistema nervoso artificiale mettendolo in relazione al primo e spiegando come vengono imitate le parti del sistema nervoso biologico da parti meccaniche e informatiche. L’argomento centrale della tesi tratta la classificazione di impatti su una lastra piana a partire da algoritmi implementati in ambiente Matlab. Attraverso l’uso di 4 script si generano dei dati ch

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Sasse, Jonathan Patrick. "Distinguishing Behavior from Highly Variable Neural Recordings Using Machine Learning." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1522755406249275.

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Gosal, Gurpreet Singh. "The use of Inverse Neural Networks in the Fast Design of Printed Lens Antennas." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32249.

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In this thesis the major objective is the implementation of the inverse neural network concept in the design of printed lens (transmitarray) antenna. As it is computationally extensive to perform full-wave simulations for entire transmitarray structure and thereafter perform optimization, the idea is to generate a design database assuming that a unit cell of the transmitarray is situated inside a 2D infinite periodic structure. This way we generate a design database of transmission coefficient by varying the unit cell parameters. Since, for the actual design, we need dimensions for each cell o

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15

Přecechtěl, Roman. "Optimalizace řízení aktivního síťového prvku." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-218166.

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The thesis deals with the use of neuronal networks for the control of telecommunication network elements. The aim of the thesis is to create a simulation model of network element with switching array with memory, in which the optimization kontrol switching array is solved by means of the neural network. All source code is created in integrated environment MATLAB. To training are used feed-forward backpropagation network. Miss achieve satisfactory result mistakes. Work apposite decision procedure given to problem and it is possible on ni tie up in an effort to find optimum solving.

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Vaško, Jan. "Využití prostředků umělé inteligence na kapitálových trzích." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2011. http://www.nusl.cz/ntk/nusl-222910.

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Diploma thesis deals with analyzing the possibility of using artificial intelligence, specifically artificial neural networks and fuzzy logic, on the capital markets as a tool to support decision making in business. The Matlab software is used for this purpose. The work is divided into three parts. The first part deals with theoretical knowledge, brief description of the current situationin is covered in a second part and the theoretical solutions are applied to the system in the third section.

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Kotol, Martin. "Neuronové modelování elektromegnetických polí uvnitř automobilů." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2018. http://www.nusl.cz/ntk/nusl-390291.

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Disertační práce se věnuje využití umělých neuronových sítí pro modelování elektromagnetických polí uvnitř automobilů. První část práce je zaměřena na analytický popis šíření elektromagnetických vlny interiérem pomocí Nortonovy povrchové vlny. Následující část práce se věnuje praktickému měření a ověření analytických modelů. Praktická měření byla zdrojem trénovacích a verifikačních dat pro neuronové sítě. Práce se zaměřuje na kmitočtová pásma 3 až 11 GHz a 55 až 65 GHz.

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Smetana, Bedřich. "Algebraizace a parametrizace přechodových relací mezi strukturovanými objekty s aplikacemi v oblasti neuronových sítí." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-433543.

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The dissertation thesis investigates the modeling of the neural network activity with a focus on a multilayer forward neural network (MLP – Multi Layer Perceptron). In this often used structure of neural networks, time-varying neurons are used, along with an analogy in modeling hyperstructures of linear differential operators. Using a finite lemma and defined hyperoperation, a hyperstructure composed of neurons is defined for a given transient function. There are examined their properties with an emphasis on structures with a layout.

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19

Momeni, Mehdi. "Feed-Forward Neural Network (FFNN) Based Optimization Of Air Handling Units: A State-Of-The-Art Data-Driven Demand-Controlled Ventilation Strategy." Thesis, 2020. http://hdl.handle.net/1805/23569.

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Indiana University-Purdue University Indianapolis (IUPUI)<br>Heating, ventilation and air conditioning systems (HVAC) are the single largest consumer of energy in commercial and residential sectors. Minimizing its energy consumption without compromising indoor air quality (IAQ) and thermal comfort would result in environmental and financial benefits. Currently, most buildings still utilize constant air volume (CAV) systems with on/off control to meet the thermal loads. Such systems, without any consideration of occupancy, may ventilate a zone excessively and result in energy waste. Previous st

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(9187742), SAYEDMOHAMMADMA VAEZ MOMENI. "FEED-FORWARD NEURAL NETWORK (FFNN) BASED OPTIMIZATION OF AIR HANDLING UNITS: A STATE-OF-THE-ART DATA-DRIVEN DEMAND-CONTROLLED VENTILATION STRATEGY." Thesis, 2020.

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Heating, ventilation and air conditioning systems (HVAC) are the single largest consumer of energy in commercial and residential sectors. Minimizing its energy consumption without compromising indoor air quality (IAQ) and thermal comfort would result in environmental and financial benefits. Currently, most buildings still utilize constant air volume (CAV) systems with on/off control to meet the thermal loads. Such systems, without any consideration of occupancy, may ventilate a zone excessively and result in energy waste. Previous studies showed that CO<sub>2</sub>-based demand-controlled vent

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21

Bahrami, Asl Babak. "Futuristic Air Compressor System Design and Operation by Using Artificial Intelligence." Thesis, 2018. http://hdl.handle.net/1805/17932.

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Indiana University-Purdue University Indianapolis (IUPUI)<br>The compressed air system is widely used throughout the industry. Air compressors are one of the most costly systems to operate in industrial plants in terms of energy consumption. Therefore, it becomes one of the primary targets when it comes to electrical energy and load management practices. Load forecasting is the first step in developing energy management systems both on the supply and user side. A comprehensive literature review has been conducted, and there was a need to study if predicting compressed air system’s load is a po

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(5931020), Babak Bahrami Asl. "FUTURISTIC AIR COMPRESSOR SYSTEM DESIGN AND OPERATION BY USING ARTIFICIAL INTELLIGENCE." Thesis, 2020.

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<div>The compressed air system is widely used throughout the industry. Air compressors are one of the most costly systems to operate in industrial plants in therms of energy consumption. Therefore, it becomes one of the primary target when it comes to electrical energy and load management practices. Load forecasting is the first step in developing energy management systems both on the supply and user side. A comprehensive literature review has been conducted, and there was a need to study if predicting compressed air system’s load is a possibility. </div><div><br></div><div>System’s load profi

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23

Huang, Hau-Luen, and 黃晧倫. "Independent Component Analysis-Based Feed-Forward Neural Network for Applications of Active Noise Control." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/73680378043527749812.

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碩士<br>中興大學<br>機械工程學系所<br>95<br>In this study, an application of active noise control (ANC) using an independent component analysis-based feed-forward neural network (FFNN) is investigated. We consider a speech source and a noise source generating mixture signals in the space. Two microphones located at two distinct places are used to measure the mixture signals. Since the signal sources and the transmission paths are unknown, we apply a FFNN with MJH algorithm（FFNN_MJH）for the measured signals to obtain estimates of the signal sources. These estimates are then used for an ANC controller such t

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林育宏, Yu-Hong Lin, and 林育宏. "Probability Distribution-Guided Water Flow-like Algorithm for Continuous Optimization: Application to Feed-forward Neural Network Training." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/b797w4.

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碩士<br>國立臺灣大學<br>工業工程學研究所<br>103<br>Water flow-like algorithm (WFA) is inspired by the nature of water flow during circulating in the physical space. Initailly, WFA was developed to be a heuristic algorithm for combinatorial optimization. Thanks to WFA’s underlying ideal, this work propose a novel version of WFA for continuous optimization, called probability distribution-guided water flow-like algorithm (PWFA). In PWFA, basins are conceptualized as subspaces in the solution space, which help subflows to stochastically move toward the lowest position (the global optimum). To imitate the behavio

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CHOU, YUEH-CHING, and 周岳慶. "Combining Particle Swarm Optimization, Gravitational Search Algorithm and Fuzzy Rule to Improve the Classification Performance for Feed-forward Neural Network." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/28zh58.

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碩士<br>國立勤益科技大學<br>工業工程與管理系<br>107<br>The Feed-forward Neural Network (FNN) is a kind of artificial neural network and has been widely used in medical diagnosis, data mining, securities market analysis and other fields. In FNN, during the learning process, the goal is to find the best combination of connection weights and biases in order to achieve the minimum error. However, in many cases, FNNs converge to local optimum but not global optimum. How to optimize the connection weights and deviations to achieve the minimum error is one of the purposes of this study. This study is to use the Univer

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Gan, Luyun. "Multi-label classification with optimal thresholding for multi-composition spectroscopic analysis." Thesis, 2019. http://hdl.handle.net/1828/11095.

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Spectroscopic analysis has several applications in physics, chemistry, bioinformatics, geophysics, astronomy, etc. It has been widely used for detecting mineral samples, gas emission, and food volatiles. Machine learning algorithms for spectroscopic analysis focus on either regression or single-label classification problems. Using multi-label classification to identify multiple chemical components from the spectrum, has not been explored. In this thesis, we implement Feed-forward Neural Network with Optimal Thresholding (FNN-OT) identifying gas species among a multi gas mixture in a cluttere

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Oba, Pius Nwachukwu. "Quality analysis modelling for development of a process controller in resistance spot welding using neural networks techniques." Thesis, 2006. http://hdl.handle.net/10539/1676.

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Student Number : 9811923K - PhD thesis - School of Mechanical Engineering - Faculty of Engineering and the Built Environment<br>Methods are presented for obtaining models used for predicting welded sample resistance and effective weld current (RMS) for desired weld diameter (weld quality) in the resistance spot welding process. These models were used to design predictive controllers for the welding process. A suitable process model forms an important step in the development and design of process controllers for achieving good weld quality with good reproducibility. Effective current, dynam

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Carvalho, João Gabriel Marques. "Electricity consumption forecast model for the DEEC based on machine learning tools." Master's thesis, 2020. http://hdl.handle.net/10316/90148.

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Dissertação de Mestrado Integrado em Engenharia Electrotécnica e de Computadores apresentada à Faculdade de Ciências e Tecnologia<br>Nesta tese apresentaremos o trabalho sobre a criação de uma rede neuronal de aprendizagem automática, capaz de realizar previsões energéticas. Com o aumento do consumo energético, devem desenvolvidas ferramentas capazes de prever o consumo. Esta necessidade levou à pesquisa deste tema.Procura-se explicar a história da aprendizagem automática, o que é a aprendizagem automática e como é que esta funciona. Também se procura explicar os seus antecedentes matemáticos,

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