Tesis sobre el tema "Dynamic optimal learning rate"
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Cheng, Martin Chun-Sheng y pjcheng@ozemail com au. "Dynamical Near Optimal Training for Interval Type-2 Fuzzy Neural Network (T2FNN) with Genetic Algorithm". Griffith University. School of Microelectronic Engineering, 2003. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20030722.172812.
Texto completoCheng, Martin Chun-Sheng. "Dynamical Near Optimal Training for Interval Type-2 Fuzzy Neural Network (T2FNN) with Genetic Algorithm". Thesis, Griffith University, 2003. http://hdl.handle.net/10072/366350.
Texto completoThesis (Masters)
Master of Philosophy (MPhil)
School of Microelectronic Engineering
Full Text
Chang, Yusun. "Dynamic Optimal Fragmentation with Rate Adaptation in Wireless Mobile Networks". Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19824.
Texto completoMoncur, Tyler. "Optimal Learning Rates for Neural Networks". BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8662.
Texto completoShu, Weihuan. "Optimal sampling rate assignment with dynamic route selection for real-time wireless sensor networks". Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=32351.
Texto completoL'attribution de calcul et de la communication ressources d'une mani`ere qui optimise les performances du syst`eme global est un aspect crucial de la gestion du syst`eme. R´eseau de capteurs sans fil pose de nouveaux d´efis en raison de la p´enurie de ressources et en temps r´eel. Travaux existants a traite distribution temps-reel probl`eme de taux d'´echantillonnage, dans un seul processeur cas et r´eseau cas de routage environment statique. Pour les r´eseaux de capteurs sans fil, afin de parvenir `a une meilleure performance globale du r´eseau, le routage devrait tre examin´e en mˆeme temps que la distribution de taux des flux individuels. Dans cet article, nous abordons le probl`eme de l'optimisation des taux d'´echantillonnage avec route s´election dynamique pour r´eseaux de capteurs sans fil. Nous modelisons le probleme comme un probl`eme d'optimisation et le r´esolvons dans le cadre de l'utilite de reseau maximisation. Sur la base de la m´ethode primal-dual et la dual d´ecomposition technique, nous concevons un algorithme distribu´e qui atteint le meilleur l'utilite de reseau globale au vu de route d´ecision dynamique et le taux distribution. Des simulations ont ´et´e r´ealis´ees pour d´emontrer l'efficience et l'efficacit´e de nos solutions propos´ees. fr
Aroh, Kosisochukwu C. "Determination of optimal conditions and kinetic rate parameters in continuous flow systems with dynamic inputs". Thesis, Massachusetts Institute of Technology, 2018. https://hdl.handle.net/1721.1/121815.
Texto completoCataloged from PDF version of thesis.
Includes bibliographical references (pages 171-185).
.The fourth industrial revolution is said to be brought about by digitization in the manufacturing sector. According to this understanding, the third industrial revolution which involved computers and automation will be further enhanced with smart and autonomous systems fueled by data and machine learning. At the research stage, an analogous story is being told in how automation and new technologies could revolutionize a chemistry laboratory. Flow chemistry is a technique that contrast with traditional batch chemistry in one aspect as a method that facilitates process automation and in small scales, delivers process improvements such as high heat and mass transfer rates. In addition to flow chemistry, analytical tools have also greatly improved and have become fully automated with potential for remote control. Over the past decade, work utilizing optimization techniques to find optimal conditions in flow chemistry have become more prevalent.
In addition, the scope of reactions performed in these systems have also increased. In the first part of this thesis, the construction of a platform capable of performing a wide range of these reactions on the lab scale is discussed. This platform was built with the capability of performing global optimizations using steady state experiments. The rest of the thesis concerns generating dynamic experiments in flow systems and using these conditions to gain more information about a reaction. The ability to use dynamic experiments to accurately determine reaction kinetics is first detailed. Through these experiments we found that only two orthogonal experiments were needed to sample the experimental space. After this an algorithm that utilizes dynamic experiments for kinetic parameter estimation problems is described. The approach here was to use dynamic experiments to first quickly sample the design space to get a reasonable estimate of the kinetic parameters.
Then steady state optimal design of experiments were used to fine tune these estimates. We observed that after initial orthogonal experiments only three more conditions were needed for accurate estimates of the multi-step reaction example. In a similar fashion, an algorithm for reaction optimization that relies on dynamic experiments is also described. The approach here extended that of adaptive response surface methodology where dynamic orthogonal experiments were performed in place of steady state experiments. When compared to steady state optimizations of multi-step reactions, a reduction by half in time needed to locate the optimum is observed. Finally, the potential issues that arise when using transient experiments in automated systems for reaction analysis are addressed. These issues include dispersion, sampling rate, reactor sizes and the rate of change of transients.
These results demonstrate a way with which technological innovation could further revolutionize the chemistry laboratory. By combining machine learning, clouding computing and efficient, high information experiments reaction data could be quickly collected, and the information gained could be maximized for future predictions or optimizations.
by Kosisochukwu C. Aroh.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Department of Chemical Engineering
Ouyang, Hua. "Optimal stochastic and distributed algorithms for machine learning". Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49091.
Texto completoLee, Jong Min. "A Study on Architecture, Algorithms, and Applications of Approximate Dynamic Programming Based Approach to Optimal Control". Diss., Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/5048.
Texto completoBountourelis, Theologos. "Efficient pac-learning for episodic tasks with acyclic state spaces and the optimal node visitation problem in acyclic stochastic digaphs". Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/28144.
Texto completoCommittee Chair: Reveliotis, Spyros; Committee Member: Ayhan, Hayriye; Committee Member: Goldsman, Dave; Committee Member: Shamma, Jeff; Committee Member: Zwart, Bert.
Singh, Manish Kumar. "Optimization, Learning, and Control for Energy Networks". Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/104064.
Texto completoDoctor of Philosophy
Massive infrastructure networks play a pivotal role in everyday human lives. A minor service disruption occurring locally in electric power, natural gas, or water networks is considered a significant loss. Uncertain demands, equipment failures, regulatory stipulations, and most importantly complicated physical laws render managing these networks an arduous task. Oftentimes, the first principle mathematical models for these networks are well known. Nevertheless, the computations needed in real-time to make spontaneous decisions frequently surpass the available resources. Explicitly identifying such problems, this dissertation extends the state of the art on three fronts: First, efficient models enabling the operators to tractably solve some routinely encountered problems are developed using fundamental and diverse mathematical tools; Second, quickly trainable machine learning based solutions are developed that enable spontaneous decision making while learning offline from sophisticated mathematical programs; and Third, control mechanisms are designed that ensure a safe and autonomous network operation without human intervention. These novel solutions are bolstered by mathematical guarantees and extensive simulations on benchmark power, water, and natural gas networks.
Fiocchi, Leonardo. "A Reinforcement Learning strategy for Satellite Attitude Control". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.
Buscar texto completoMBITI, JOHN N. "Deep learning for portfolio optimization". Thesis, Linnéuniversitetet, Institutionen för matematik (MA), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-104567.
Texto completoHeng, Jeremy. "On the use of transport and optimal control methods for Monte Carlo simulation". Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:6cbc7690-ac54-4a6a-b235-57fa62e5b2fc.
Texto completoPop, Ionel. "Détection des événements rares dans des vidéos". Thesis, Lyon 2, 2010. http://www.theses.fr/2010LYO22023.
Texto completoThe growing number of video data makes often difficult, even impossible, any attemptof watching them entirely. In the context of automatic analysis of videos, a recurring request is to identify moments in the video when something unusual happens.We propose several algorithms to identify unusual events, making the hypothesis that these events have a low probability. We address several types of events, from those generates by moving areas to the trajectories of objects tracked. In the first part of the study, we build a simple tracking system. We propose several measures of similarity between trajectories. These measures give an estimate of the similarity of trajectories by taking into account both spatial and/or temporal aspects. It is possible to differentiate between objects moving on the same path, but with different speeds. Based on these measures, we build models of trajectories representing the common behavior of objects, so that we can identify those that are abnormal.We noticed that the tracking yields bad results, especially in crowd situations. Therefore, we use the optical flow vectors to build a movement model based on a codebook. This model stores the preferred movement directions for each pixel. It is possible to identify abnormal movement at pixel-level, without having to use a tracker. By using temporal coherence, we can further improve the detection rate, affected by errors of estimation of optic flow. In a second step, we change the method of construction of this model. With the new approach, we can extract higher-level features — the equivalent trajectories, but still without the notion of object tracking. In this situation, we can reuse partial trajectory analysis to detect rare events.All aspects presented in this study have been implemented. In addition, we have design some applications, like predicting the trajectories of visible objects or storing and retrieving tracked objects in a database
Monteiro, Rocha Lima Bruno. "Object Surface Exploration Using a Tactile-Enabled Robotic Fingertip". Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39956.
Texto completoGonzalez, Karen. "Contribution à l’étude des processus markoviens déterministes par morceaux : étude d’un cas-test de la sûreté de fonctionnement et problème d’arrêt optimal à horizon aléatoire". Thesis, Bordeaux 1, 2010. http://www.theses.fr/2010BOR14139/document.
Texto completoPiecewise Deterministic Markov Processes (PDMP's) have been introduced inthe literature by M.H.A. Davis as a general class of stochastics models. PDMP's area family of Markov processes involving deterministic motion punctuated by randomjumps. In a first part, PDMP's are used to compute probabilities of top eventsfor a case-study of dynamic reliability (the heated tank system) with two di#erentmethods : the first one is based on the resolution of the differential system giving thephysical evolution of the tank and the second uses the computation of the functionalof a PDMP by a system of integro-differential equations. In the second part, wepropose a numerical method to approximate the value function for the optimalstopping problem of a PDMP. Our approach is based on quantization of the post-jump location and inter-arrival time of the Markov chain naturally embedded in thePDMP, and path-adapted time discretization grids. It allows us to derive boundsfor the convergence rate of the algorithm and to provide a computable ε-optimalstopping time
Pahkasalo, Carolina y André Sollander. "Adaptive Energy Management Strategies for Series Hybrid Electric Wheel Loaders". Thesis, Linköpings universitet, Fordonssystem, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166284.
Texto completoQu, Zheng. "Nonlinear Perron-Frobenius theory and max-plus numerical methods for Hamilton-Jacobi equations". Palaiseau, Ecole polytechnique, 2013. http://pastel.archives-ouvertes.fr/docs/00/92/71/22/PDF/thesis.pdf.
Texto completoDynamic programming is one of the main approaches to solve optimal control problems. It reduces the latter problems to Hamilton-Jacobi partial differential equations (PDE). Several techniques have been proposed in the literature to solve these PDE. We mention, for example, finite difference schemes, the so-called discrete dynamic programming method or semi-Lagrangian method, or the antidiffusive schemes. All these methods are grid-based, i. E. , they require a discretization of the state space, and thus suffer from the so-called curse of dimensionality. The present thesis focuses on max-plus numerical solutions and convergence analysis for medium to high dimensional deterministic optimal control problems. We develop here max-plus based numerical algorithms for which we establish theoretical complexity estimates. The proof of these estimates is based on results of nonlinear Perron-Frobenius theory. In particular, we study the contraction properties of monotone or non-expansive nonlinear operators, with respect to several classical metrics on cones (Thompson's metric, Hilbert's projective metric), and obtain nonlinear or non-commutative generalizations of the "ergodicity coefficients" arising in the theory of Markov chains. These results have applications in consensus theory and also to the generalized Riccati equations arising in stochastic optimal control
Ferreira, Ernesto Franklin Marçal. "Melhorias de estabilidade numérica e custo computacional de aproximadores de funções valor de estado baseados em estimadores RLS para projeto online de sistemas de controle HDP-DLQR". Universidade Federal do Maranhão, 2016. http://tedebc.ufma.br:8080/jspui/handle/tede/1687.
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The development and the numerical stability analysis of a new adaptive critic algorithm to approximate the state-value function for online discrete linear quadratic regulator (DLQR) optimal control system design based on heuristic dynamic programming (HDP) are presented in this work. The proposed algorithm makes use of unitary transformations and QR decomposition methods to improve the online learning e-ciency in the critic network through the recursive least-squares (RLS) approach. The developed learning strategy provides computational performance improvements in terms of numerical stability and computational cost which aim at making possible the implementations in real time of optimal control design methodology based upon actor-critic reinforcement learning paradigms. The convergence behavior and numerical stability of the proposed online algorithm, called RLSµ-QR-HDP-DLQR, are evaluated by computational simulations in three Multiple-Input and Multiple-Output (MIMO) models, that represent the automatic pilot of an F-16 aircraft of third order, a fourth order RLC circuit with two input voltages and two controllable voltage levels, and a doubly-fed induction generator with six inputs and six outputs for wind energy conversion systems.
Neste trabalho, apresenta-se o desenvolvimento e a análise da estabilidade numérica de um novo algoritmo crítico adaptativo para aproximar a função valor de estado para o projeto do sistema de controle ótimo online, utilizando o regulador linear quadrático discreto (DLQR), com base em programação dinâmica heurística (HDP). O algoritmo proposto faz uso de transformações unitárias e métodos de decomposição QR para melhorar a e-ciência da aprendizagem online na rede crítica por meio da abordagem dos mínimos quadrados recursivos (RLS). A estratégia de aprendizagem desenvolvida fornece melhorias no desempenho computacional em termos de estabilidade numérica e custo computacional, que visam tornar possíveis as implementações em tempo real da metodologia do projeto de controle ótimo com base em paradigmas de aprendizado por reforço ator-crítico. O comportamento de convergência e estabilidade numérica do algoritmo online proposto, denominado RLSµ-QR-HDP-DLQR, são avaliados por meio de simulações computacionais em três modelos Múltiplas-Entradas e Múltiplas-Saídas (MIMO), que representam o piloto automático de uma aeronave F-16 de terceira ordem, um circuito de quarta ordem RLC com duas tensões de entrada e dois níveis de tensão controláveis, e um gerador de indução duplamente alimentados com seis entradas e seis saídas para sistemas de conversão de energia eólica.
Yu, Yi. "Radio Resource Planning in Low Power Wide Area IoT Networks". Electronic Thesis or Diss., Paris, CNAM, 2021. http://www.theses.fr/2021CNAM1287.
Texto completoIn this thesis, we focus on radio resource planning issues for low power wide area networks based on NB-IoT and LoRa technologies. In both cases, the average behavior of the network is considered by assuming the sensors and the collectors are distributed according to independent random Poisson Point Process marked by the channel randomness. For the NB-IoT, we elaborate a statistical dimensioning model that estimates the number of radio resources in the network depending on the tolerated delay access, the density of active nodes, the collectors, and the antenna configuration with single and multi-user transmission. For the LoRa network, we propose a multi-sub band allocation technique to mitigate the high level of interference induced by nodes that transmit with the same spreading factor. To dynamically allocate the spreading factor and the power, we present a Q-learning multi-agent approach to improve the energy efficiency
RÊGO, Patrícia Helena Moraes. "Aprendizagem por Reforço e Programação Dinâmica Aproximada para Controle Ótimo: Uma Abordagem para o Projeto Online do Regulador Linear Quadrático Discreto com Programação Dinâmica Heurística Dependente de Estado e Ação". Universidade Federal do Maranhão, 2014. http://tedebc.ufma.br:8080/jspui/handle/tede/1879.
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In this thesis a proposal of an uni ed approach of dynamic programming, reinforcement learning and function approximation theories aiming at the development of methods and algorithms for design of optimal control systems is presented. This approach is presented in the approximate dynamic programming context that allows approximating the optimal feedback solution as to reduce the computational complexity associated to the conventional dynamic programming methods for optimal control of multivariable systems. Speci cally, in the state and action dependent heuristic dynamic programming framework, this proposal is oriented for the development of online approximated solutions, numerically stable, of the Riccati-type Hamilton-Jacobi-Bellman equation associated to the discrete linear quadratic regulator problem which is based on a formulation that combines value function estimates by means of a RLS (Recursive Least-Squares) structure, temporal di erences and policy improvements. The development of the proposed methodologies, in this work, is focused mainly on the UDU T factorization that is inserted in this framework to improve the RLS estimation process of optimal decision policies of the discrete linear quadratic regulator, by circumventing convergence and numerical stability problems related to the covariance matrix ill-conditioning of the RLS approach.
Apresenta-se nesta tese uma proposta de uma abordagem uni cada de teorias de programação dinâmica, aprendizagem por reforço e aproximação de função que tem por objetivo o desenvolvimento de métodos e algoritmos para projeto online de sistemas de controle ótimo. Esta abordagem é apresentada no contexto de programação dinâmica aproximada que permite aproximar a solução de realimentação ótima de modo a reduzir a complexidade computacional associada com métodos convencionais de programação dinâmica para controle ótimo de sistemas multivariáveis. Especi camente, no quadro de programação dinâmica heurística e programação dinâmica heurística dependente de ação, esta proposta é orientada para o desenvolvimento de soluções aproximadas online, numericamente estáveis, da equação de Hamilton-Jacobi-Bellman do tipo Riccati associada ao problema do regulador linear quadrático discreto que tem por base uma formulação que combina estimativas da função valor por meio de uma estrutura RLS (do inglês Recursive Least-Squares), diferenças temporais e melhorias de política. O desenvolvimento das metodologias propostas, neste trabalho, tem seu foco principal voltado para a fatoração UDU T que é inserida neste quadro para melhorar o processo de estimação RLS de políticas de decisão ótimas do regulador linear quadrá- tico discreto, contornando-se problemas de convergência e estabilidade numérica relacionados com o mal condicionamento da matriz de covariância da abordagem RLS.
Kumar, Tushar. "Characterizing and controlling program behavior using execution-time variance". Diss., Georgia Institute of Technology, 2016. http://hdl.handle.net/1853/55000.
Texto completoAndrade, Gustavo Araújo de. "PROGRAMAÇÃO DINÂMICA HEURÍSTICA DUAL E REDES DE FUNÇÕES DE BASE RADIAL PARA SOLUÇÃO DA EQUAÇÃO DE HAMILTON-JACOBI-BELLMAN EM PROBLEMAS DE CONTROLE ÓTIMO". Universidade Federal do Maranhão, 2014. http://tedebc.ufma.br:8080/jspui/handle/tede/517.
Texto completoIn this work the main objective is to present the development of learning algorithms for online application for the solution of algebraic Hamilton-Jacobi-Bellman equation. The concepts covered are focused on developing the methodology for control systems, through techniques that aims to design online adaptive controllers to reject noise sensors, parametric variations and modeling errors. Concepts of neurodynamic programming and reinforcement learning are are discussed to design algorithms where the context of a given operating point causes the control system to adapt and thus present the performance according to specifications design. Are designed methods for online estimation of adaptive critic focusing efforts on techniques for gradient estimating of the environment value function.
Neste trabalho o principal objetivo é apresentar o desenvolvimento de algoritmos de aprendizagem para execução online para a solução da equação algébrica de Hamilton-Jacobi-Bellman. Os conceitos abordados se concentram no desenvolvimento da metodologia para sistemas de controle, por meio de técnicas que tem como objetivo o projeto online de controladores adaptativos são projetados para rejeitar ruídos de sensores, variações paramétricas e erros de modelagem. Conceitos de programação neurodinâmica e aprendizagem por reforço são abordados para desenvolver algoritmos onde a contextualização de determinado ponto de operação faz com que o sistema de controle se adapte e, dessa forma, apresente o desempenho de acordo com as especificações de projeto. Desenvolve-se métodos para a estimação online do crítico adaptativo concentrando os esforços em técnicas de estimação do gradiente da função valor do ambiente.
Alizamir, Saed. "Essays on Optimal Control of Dynamic Systems with Learning". Diss., 2013. http://hdl.handle.net/10161/8066.
Texto completoThis dissertation studies the optimal control of two different dynamic systems with learning: (i) diagnostic service systems, and (ii) green incentive policy design. In both cases, analytical models have been developed to improve our understanding of the system, and managerial insights are gained on its optimal management.
We first consider a diagnostic service system in a queueing framework, where the service is in the form of sequential hypothesis testing. The agent should dynamically weigh the benefit of performing an additional test on the current task to improve the accuracy of her judgment against the incurred delay cost for the accumulated workload. We analyze the accuracy/congestion tradeoff in this setting and fully characterize the structure of the optimal policy. Further, we allow for admission control (dismissing tasks from the queue without processing) in the system, and derive its implications on the structure of the optimal policy and system's performance.
We then study Feed-in-Tariff (FIT) policies, which are incentive mechanisms by governments to promote renewable energy technologies. We focus on two key network externalities that govern the evolution of a new technology in the market over time: (i) technological learning, and (ii) social learning. By developing an intertemporal model that captures these dynamics, we investigate how lawmakers should leverage on such effects to make FIT policies more efficient. We contrast our findings against the current practice of FIT-implementing jurisdictions, and also determine how the FIT regimes should depend on specific technology and market characteristics.
Dissertation
LI, JHIH-CHENG y 黎致呈. "Dynamic Intraday Exchange Rate Forecasting using Machine Learning Methods". Thesis, 2017. http://ndltd.ncl.edu.tw/handle/9d3wcx.
Texto completo輔仁大學
統計資訊學系應用統計碩士班
105
This study applies Random Forest model to forecasting the exchange rate of USD、JPY、EUR、CNY. This study uses spot buying rate of mean, standard deviation, maximum, minimum, starting value, and end value every 30 minutes as the research variables.The empirical interval is from May 12, 2016 to September 25, 2016. The neural network and support vector regression are used as the benchmarks. The empirical results show that the use of the intraday data as the training sample can reduce the prediction error, and the random forest is better than the neural network and the support vector regression.
Walton, Zachary. "Optimal Control of Perimeter Patrol Using Reinforcement Learning". Thesis, 2011. http://hdl.handle.net/1969.1/ETD-TAMU-2011-05-9520.
Texto completoBuckland, Kenneth M. "Optimal control of dynamic systems through the reinforcement learning of transition points". Thesis, 1994. http://hdl.handle.net/2429/6896.
Texto completoTsai, Ding-Yu y 蔡定宇. "A Study of the Dynamic Programming on Optimal Contract Capacity for a Time-of-Use Rate User". Thesis, 2011. http://ndltd.ncl.edu.tw/handle/f68j6m.
Texto completo國立臺北科技大學
電機工程系優質電力產業研發專班
99
Currently, there are four parts of TAIPOWER Company’s electricity cost are composed of demand charge, energy charge, power factor charge and penalty charge. In order to save power rate spending, the research reduce electricity cost on demand charge and penalty charge but we don’t need to buy any machine. The demand charge and penalty charge that are obtained according to contract capacity with TAIPOWER Company. Therefore, how to make an optimal contract capacity is the common problem for the consumer to face. Because general consumers are short of the judgment to revise optimal contract capacity, therefore sign unsuitable contract capacity constantly. When you ask the views of TAIPOWER, then they will give you some advices about contract capacity, but they won’t remind you that it’s suitability or not for the contract capacity in the future. Therefore, Best way to save cost is to check the appropriateness of contract capacity frequently. The thesis major is researching a dynamic optimal contract capacity. First, the research get optimal contract capacity by sequential search and use dynamic programming to adjust the monthly contract capacity, to calculate the optimal contract capacity. The research result, it’s effective to adjust the contract capacity for cost down by dynamic programming. The research could be a reference for seasonal and power of demand varies greatly user. To sure their own set of contract capacity is appropriate.
Chou, Hsiang-Chai y 周祥在. "Dynamic Recursive-Based Optimal Learning Fuzzy Systems Design-Constructing Financial Time-series Prediction System". Thesis, 2009. http://ndltd.ncl.edu.tw/handle/19935391766580764909.
Texto completo國立金門技術學院
電資研究所
97
The tremendous sudden variation and complex nonlinear dimensionality of the stock prices is a big challenging task in stock markets predicting and trading problems. In this thesis, the dynamic recursive-based optimal learning algorithm is designed to build financial time-series fuzzy prediction systems. The fuzzy system with suitable human-beings decision rules is determined to identify behavior of the discussed time-series stock data. Containing with the adapt ability of the fuzzy inference system, the proposed fuzzy prediction systems can reduce the affect in some of non-quantifiable political, social noise and interference. The objective of this stock prediction system is to reduce investment risk, and enable investors to reap the maximum profit. The dynamic clustering method is first applied to configure the initial architecture of the fuzzy prediction system. Selected fuzzy-rules number is equal to the number of cluster centers and cluster center results will be assigned to locate the initial center position of the membership function. The particle swarm optimizationm (PSO) and recursive least square (RLS) methods are integrated to tune fuzzy system parameters for approximating toward the trade feature of the collected Taiwan's weighted index. Experiment results compared with other machine learning schemes for the prediction of the stock prices and tracking trend are illustrated to demonstrate our proposed system having better sell and buy winning stratagem.
Chen, Kuan-Zung y 陳冠榮. "Using credit assignment and GBF with dynamic learning rate to enhance the ability of low-dimensional CMAC". Thesis, 2004. http://ndltd.ncl.edu.tw/handle/14666150716669599215.
Texto completo大同大學
電機工程學系(所)
92
Conventional CMAC has a memory size problem at high dimensional input space. Using the neural network structure composed of small CMACs can efficiently solve this problem. By using the advantage of credit assignment and Gaussian basis function, we use it to improve the performance of the neural network structure composed of small CMACs. The simulation result shows that it still has weakness. The dynamic learning rate and repeat training are the concepts from conventional CMAC. We use the dynamic learning rate and repeat training to overcome the weakness. The simulation shows that dynamic learning rate indeed have better performance, and dynamic learning rate with Gaussian basis function could achieve the best result.
Wang, Ko-Jie y 王科傑. "Adaptive PD Fuzzy Control with Dynamic Learning Rate for Two-Wheeled Balancing Six Degrees of Freedom Robotic Arm". Thesis, 2014. http://ndltd.ncl.edu.tw/handle/6grm9v.
Texto completo國立臺灣科技大學
電機工程系
102
This thesis puts forward a new design of robot system, two-wheeled balancing six degrees of freedom robotic arm with an adaptive PD fuzzy controller with dynamic learning rate. The robot system considered is to combine a six-degrees-of-freedom robotic arm and a two-wheeled balancing robot. The robot system is to equip the robotic am with mobility. But due to the motion of the arm, the stability issue becomes a challenge task for the system. This study employs the adaptive fuzzy control idea to provide suitable controller for the system. The proposed controller uses PD controllers for fuzzy system outputs. From our experiments, it is observed that to have nice performance, the learning rate is positive correlated to the extra speed. Thus, in this study, we propose to use a learning rate as a function of speed for the adaptive system. In order to show the superiority of the proposed controller, in our experiments, PD, fuzzy, adaptive fuzzy, PD fuzzy and adaptive PD fuzzy controllers are also employed. In the experiments, extra speed and extra moment of inertia with extra torque conditions are included. From those experiments, it can found the proposed approach indeed has better performance overall. In addition, the controller is used in the real system and it is compared to the use of PD controller which is commonly used. Again, our controller is better. In other words, the robot system can work with the proposed controller well to enhance control of balancing the robot.
Sekgwelea, Sello Molefe. "Dynamic approach in the application of information communication technologies models in the provision of flexible learning for distance education". Thesis, 2007. http://hdl.handle.net/10500/2535.
Texto completoEducational Studies
(D. Ed. (Curriculum Studies))
Sindhu, P. R. "Algorithms for Product Pricing and Energy Allocation in Energy Harvesting Sensor Networks". Thesis, 2014. http://etd.iisc.ernet.in/2005/3505.
Texto completo(8072417), Braiden M. Frantz. "Active Shooter Mitigation for Open-Air Venues". Thesis, 2021.
Buscar texto completoThis dissertation examines the impact of active shooters upon patrons attending large outdoor events. There has been a spike in shooters targeting densely populated spaces in recent years, to include open-air venues. The 2019 Gilroy Garlic Festival was selected for modeling replication using AnyLogic software to test various experiments designed to reduce casualties in the event of an active shooter situation. Through achievement of validation to produce identical outcomes of the real-world Gilroy Garlic Festival shooting, the researcher established a reliable foundational model for experimental purposes. This active shooter research project identifies the need for rapid response efforts to neutralize the shooter(s) as quickly as possible to minimize casualties. Key findings include the importance of armed officers patrolling event grounds to reduce response time, the need for adequate exits during emergency evacuations, incorporation of modern technology to identify the shooter’s location, and applicability of a 1:548 police to patron ratio.