Дисертації з теми "Feedback neuron"
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Dickson, Scott M. "Stochastic neural network dynamics : synchronisation and control." Thesis, Loughborough University, 2014. https://dspace.lboro.ac.uk/2134/16508.
Повний текст джерелаShin, Jiyun. "Perirhinal feedback input controls neocortical memory formation via layer 1." Doctoral thesis, Humboldt-Universität zu Berlin, 2021. http://dx.doi.org/10.18452/22312.
Повний текст джерелаDeclarative memory relies on interactions between the medial temporal lobe (MTL) and neocortex. However, due the distributed nature of neocortical networks, cellular targets and mechanisms of memory formation in the neocortex remain elusive. In the six-layered mammalian neocortex, top-down inputs converge on its outermost layer, layer 1 (L1). We examined how layer-specific top-down inputs from MTL modulate neocortical activity during memory formation. We first adapted a cortical- and hippocampal-dependent learning paradigm, in which animals learned to associate direct cortical microstimulation and reward, and characterized the learning behavior of rats and mice. We next showed that neurons in the deep layers of the perirhinal cortex not only provide monosynaptic inputs to L1 of the primary somatosensory cortex (S1), where microstimulation was presented, but also actively reflect the behavioral outcome. Chemogenetic suppression of perirhinal inputs to L1 of S1 disrupted early memory formation but did not affect animals’ performance after learning. The learning was followed by an emergence of a distinct subpopulation of layer 5 (L5) pyramidal neurons characterized by high-frequency burst firing, which could be reduced by blocking perirhinal inputs to L1. Interestingly, a similar proportion of apical dendrites (~10%) of L5 pyramidal neurons also displayed significantly enhanced calcium (Ca2+) activity during memory retrieval in expert animals. Importantly, disrupting dendritic Ca2+ activity impaired learning, suggesting that apical dendrites of L5 pyramidal neurons have a critical role in neocortical memory formation. Taken together, these results suggest that MTL inputs control learning via a perirhinal-mediated gating process in L1, manifested by elevated dendritic Ca2+ activity and burst firing in L5 pyramidal neurons. The present study provides insights into cellular mechanisms of learning and memory representations in the neocortex.
Kromer, Justus Alfred. "Noise in adaptive excitable systems and small neural networks." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät, 2017. http://dx.doi.org/10.18452/17683.
Повний текст джерелаNeurons are excitable systems. Their responses to excitations above a certain threshold are spikes. Usually, spike generation is shaped by several feedback mechanisms that can act on slow time scales. These can lead to phenomena such as spike-frequency adaptation, reverse spike-frequency adaptation, or bursting. In addition to these, neurons are subject to several sources of noise and interact with other neurons, in the connected complexity of a neural network. Yet how does the interplay of feedback mechanisms, noise as well as interaction with other neurons affect spike generation? This thesis examines how spike generation in noise-driven excitable systems is influenced by slow feedback processes and coupling to other excitable systems. To this end, spike generation in three setups is considered: (i) in a single excitable system, which is complemented by a slow feedback mechanism, (ii) in a set of coupled excitable systems, and (iii) in a set of strongly-coupled bursting neurons. In each of these setups, the statistics of spiking is investigated by a combination of analytical methods and computer simulations. The main result of the first setup is that the interplay of strong positive (excitatory) feedback and noise leads to noise-controlled bistability. It enables excitable systems to switch between different modes of spike generation. In (ii), spike generation is strongly affected by the choice of the coupling strengths and the number of connections. Analytical approximations are derived that relate the number of connections to the firing rate and the spike train variability. In (iii), it is found that negative (inhibitory) feedback causes very irregular behavior of the isolated bursters, while strong coupling to the network regularizes the bursting.
Gill, Jeffrey Paul. "Neural Correlates of Adaptive Responses to Changing Load in Feeding Aplysia." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1579795905638273.
Повний текст джерелаNewman, Jonathan P. "Optogenetic feedback control of neural activity." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/52973.
Повний текст джерелаSutherland, Connie. "Spatio-temporal feedback in stochastic neural networks." Thesis, University of Ottawa (Canada), 2007. http://hdl.handle.net/10393/27559.
Повний текст джерелаWilliams, Ian. "Methods and microelectronics for proprioceptive neural feedback." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/24566.
Повний текст джерелаFumuro, Tomoyuki. "Bereitschaftspotential augmentation by neuro-feedback training in Parkinson's disease." Kyoto University, 2013. http://hdl.handle.net/2433/174832.
Повний текст джерелаHabte, Samson. "Snap-drift neural computing for intelligent diagnostic feedback." Thesis, London Metropolitan University, 2017. http://repository.londonmet.ac.uk/1247/.
Повний текст джерелаAndréasson, Per. "Emotional Empathy, Facial Reactions, and Facial Feedback." Doctoral thesis, Uppsala universitet, Institutionen för psykologi, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-126825.
Повний текст джерелаWang, Liren. "An approach to neuro-fuzzy feedback control in statistical process control." Thesis, University of South Wales, 2001. https://pure.southwales.ac.uk/en/studentthesis/an-approach-to-neurofuzzy-feedback-control-in-statistical-process-control(7d9c736f-e85d-4873-a6bb-9bcea107d371).html.
Повний текст джерелаLongtin, André. "Nonlinear oscillations, noise and chaos in neural delayed feedback." Thesis, McGill University, 1989. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=74311.
Повний текст джерелаYang, Bong-Jun. "Adaptive Output Feedback Control of Flexible Systems." Diss., Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/5248.
Повний текст джерелаKutay, Ali Turker. "Neural Network Based Adaptive Output Feedback Control: Applications and Improvements." Diss., Available online, Georgia Institute of Technology, 2005, 2005. http://etd.gatech.edu/theses/available/etd-11282005-122234/.
Повний текст джерелаJ. Eric Corban, Committee Member ; Panagiotis Tsiotras, Committee Member ; Eric N. Johnson, Committee Member ; Nader Sadegh, Committee Member ; Anthony J. Calise, Committee Chair.
Fyfe, Colin. "Negative feedback as an organising principle for artificial neural networks." Thesis, University of Strathclyde, 1995. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=21390.
Повний текст джерелаFranklin, D. R. "Neural networks for visual feedback control of an industrial robot." Thesis, University of Cambridge, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.599180.
Повний текст джерелаRollins, Elizabeth S. M. Massachusetts Institute of Technology. "Optimization of neural network feedback control systems using automatic differentiation." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/59691.
Повний текст джерелаCataloged from PDF version of thesis.
Includes bibliographical references (p. 95-97).
Optimal control problems can be challenging to solve, whether using analytic or numerical methods. This thesis examines the application of an adjoint method for optimal feedback control, which combines various algorithmic techniques into an original numerical method. In the method investigated here, a neural network defines the control input in both trajectory and feedback control optimization problems. The weights of the neural network that minimize a cost function are determined by an unconstrained optimization routine. By using automatic differentiation on the code that evaluates the cost function, the gradient of the cost with respect to the weights is obtained for the gradient search phase of the optimization process. Automatic differentiation is more efficient than hand-differentiating code for the user and provides exact gradients, allowing the optimization of the neural network weights to proceed more rapidly. Another benefit of this method comes from its use of neural networks, which are able to represent complex feedback control policies, because they are general nonlinear function approximators. Neural networks also have the potential to be generalizable, meaning that a control policy found using a sufficiently rich training set will often work well for other initial conditions outside of the training set. Finally, the software implementation is modular, which means that the user only needs to adjust a few codes in order to set up the method for a specific problem. The application of the adjoint method to three control problems with known solutions demonstrates the ability of the method to determine neural networks that produce near-optimal trajectories and control policies.
by Elizabeth Rollins.
S.M.
Weaver, Adam L. "The functional roles of the Lateral Pyloric and Ventricular Dilator neurons in the pyloric network of the lobster, Panulirus interruptus." Ohio : Ohio University, 2002. http://www.ohiolink.edu/etd/view.cgi?ohiou1010521587.
Повний текст джерелаWilson, Luke. "The influence of feedback connections on the dynamics of populations of spiking neurons /." Leeds : University of Leeds, School of Computer Studies, 2008. http://www.comp.leeds.ac.uk/fyproj/reports/0708/Wilson.pdf.
Повний текст джерелаJiang, Yiwu. "Neural network based feedback linearization control of an unmanned aerial vehicle." Thesis, University of Ottawa (Canada), 2005. http://hdl.handle.net/10393/26937.
Повний текст джерелаHassibi, Khosrow M. "A study of the application of neural networks to feedback linearization." Case Western Reserve University School of Graduate Studies / OhioLINK, 1991. http://rave.ohiolink.edu/etdc/view?acc_num=case1055527640.
Повний текст джерелаTukhlina, Natalia. "Feedback control of complex oscillatory systems." Phd thesis, Universität Potsdam, 2008. http://opus.kobv.de/ubp/volltexte/2008/1854/.
Повний текст джерелаIn der vorliegenden Dissertation wird eine Näherung entwickelt, die eine effiziente Kontrolle verschiedener Systeme wie verrauschten oder chaotischen Oszillatoren und Neuronenensembles ermöglicht. Diese Näherung wird durch eine einfache lineare Rückkopplungsschleife implementiert. Die Dissertation besteht aus zwei Teilen. Ein Teil der Arbeit ist der Anwendung der vorgeschlagenen Technik auf eine Population von Neuronen gewidmet, mit dem Ziel ihre synchrone Dynamik zu unterdrücken. Der zweite Teil ist auf die Untersuchung der linearen Feedback-Kontrolle der Kohärenz eines verrauschten oder chaotischen, selbst erregenden Oszillators gerichtet. Zunächst widmen wir uns dem Problem, die Synchronisation in einer großen Population von aufeinander wirkenden Neuronen zu unterdrücken. Da angenommen wird, dass das Auftreten pathologischer Gehirntätigkeit, wie im Falle der Parkinsonschen Krankheit oder bei Epilepsie, auf die Synchronisation großer Neuronenpopulation zurück zu führen ist, ist das Verständnis dieser Prozesse von tragender Bedeutung. Die Standardtherapie bei derartigen Erkrankungen besteht in einer dauerhaften, hochfrequenten, intrakraniellen Hirnstimulation mittels implantierter Elektroden (Deep Brain Stimulation, DBS). Trotz der Wirksamkeit solcher Stimulationen können verschiedene Nebenwirkungen auftreten, und die Mechanismen, die der DBS zu Grunde liegen sind nicht klar. In meiner Arbeit schlage ich eine effiziente und einfache Kontrolltechnik vor, die die Synchronisation in einem Neuronenensemble durch eine minimierte Anregung unterdrückt und minimalinvasiv ist, da die Anregung stoppt, sobald der Tremor erfolgreich unterdrückt wurde. Diese Technik der "schwindenden Anregung" wäre ein nützliches Werkzeug der experimentellen Neurowissenschaft. Desweiteren stellt die Kontrolle der kollektiven Dynamik in einer großen Population von Einheiten ein interessantes physikalisches Problem dar. Der Grundansatz der Näherung ist eng mit dem klassischen Problem der Schwingungstheorie verwandt - der Interaktion eines selbst erregenden (aktiven) Oszillators und einer passiven Last, dem Resonator. Ich betrachte den deutlich komplexeren Fall eines aktiven Mediums, welches aus vielen tausenden Oszillatoren besteht. Durch Kopplung dieses Mediums an einen speziell hierür konzipierten, passiven Oszillator kann man die kollektive Bewegung des Ensembles kontrollieren, um diese zu erhöhen oder zu unterdrücken. Mit Hinblick auf eine möglichen Anwendung im Bereich der Neurowissenschaften, konzentriere ich mich hierbei auf das Problem der Unterdrückung. Im zweiten Teil wird die Wirksamkeit dieses Unterdrückungsschemas im Rahmen eines komplexeren Falles, bei dem die Population von Neuronen, die einen unerwünschten Rhythmus erzeugen, aus zwei nicht überlappenden Subpopulationen besteht, dargestellt. Zunächst wird eine der beiden Subpopulationen durch Stimulation beeinflusst und die kollektive Aktivität an der zweiten Subpopulation gemessen. Im Allgemeinen kann sich die zweite Subpopulation sowohl aktiv als auch passiv verhalten. Beide Fälle werden eingehend betrachtet. Anschließend werden die möglichen Anwendungen der vorgeschlagenen Technik besprochen. Danach werden verschiedene Betrachtungen über den Einfluss des externen linearen Feedbacks auf die Kohärenz eines verrauschten oder chaotischen selbst erregenden Oszillators angestellt. Kohärenz ist eine Grundeigenschaft schwingender Systeme und spielt ein tragende Rolle bei der Konstruktion von Uhren, Generatoren oder Lasern. Die Kohärenz eines verrauschten Grenzzyklus Oszillators im Sinne der Phasendynamik wird durch die Phasendiffusionskonstante bewertet, die ihrerseits zur Breite der spektralen Spitze von Schwingungen proportional ist. Viele chaotische Oszillatoren können im Rahmen der Phasendynamik beschrieben werden, weshalb ihre Kohärenz auch über die Phasendiffusionskonstante gemessen werden kann. Die analytische Theorie eines allgemeinen linearen Feedbacks in der Gaußschen, als auch in der linearen, Näherung wird entwickelt und durch numerische Ergebnisse gestützt.
Dall´Agnol, Marcelo. "Aplicação de controladores feedback em sistema experimental de refrigeração e desenvolvimento de modelo preditivo baseado em redes neurais." [s.n.], 2010. http://repositorio.unicamp.br/jspui/handle/REPOSIP/266964.
Повний текст джерелаDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Química
Made available in DSpace on 2018-08-16T02:02:41Z (GMT). No. of bitstreams: 1 Dall´Agnol_Marcelo_M.pdf: 3167173 bytes, checksum: fc8b21f6fe3204996f4d61c2febac662 (MD5) Previous issue date: 2010
Resumo: O presente trabalho teve por objetivo propor uma sistemática para identificação de um sistema de refrigeração dotado de compressor e bomba de fluido secundário de rotação variável, para a aplicação futura de alternativas de controle multivariáveis com foco na redução do consumo de energia e otimização do coeficiente de performance (COP) de tais sistemas. Uma primeira etapa de ensaios experimentais foi realizada para gerar um conhecimento prévio do sistema, caracterizando-o como não-linear. Foram utilizadas diversas arquiteturas de redes neurais artificiais (RNA) para gerar modelos não-lineares MIMO (Multiple Input-Multiple Output), através do software MATLAB® para fazer a predição das temperaturas de evaporação e do fluido secundário. Para treinamento da rede neural foram utilizados dados de ensaios em malha aberta e malha fechada com um controlador PID desenvolvido especialmente para este fim. Neste sistema de controle PID foram utilizadas técnicas como anti-windup e derivative-kick na tentativa de melhorar a atuação de tal controlador. O PID, apesar de conseguir controlar o sistema em torno de 2°C, mostrou-se pouco eficiente quando eram aplicados degraus no set-point e na carga elétrica. A modelagem não-linear resultou em uma rede neural com arquitetura 11x2x8x2 que foi capaz de prever as duas temperaturas com precisão de mais ou menos 0,5°C, mesmo quando o sistema de refrigeração se manteve instável.
Abstract: The main aim of the present work was to propose a systematic identification procedure for an experimental refrigeration system operating under variable rotation speed compressor and pump of secondary fluid. This work will find future applications in alternative multivariable control development with focus on reducing energy consumption and on the optimization of the coefficient of performance (COP) of such systems. The nonlinear feature of the system was proved by means of imposed speed disturbances process reaction curves. Using the software MATLAB? Various artificial neural networks (ANN) architectures were used to generate non-linear models with Multiple-Input-Multiple-Output (MIMO) variables, in order to predict the evaporation and the secondary fluid temperatures. Open-loop and also closed loop assays were carried out and the experimental data used in the neural network training procedure. A specially designed PID, using anti-windup and derivative-kick techniques, was employed in the closed-loop runs. This conventional temperature controller showed an acceptable off-set of 2°C, however some instability occurred when set point and thermal load changed. The best non-linear modeling resulted in a neural network with architecture 11x2x8x2. This neural model was able to predict the two temperatures with precision of about 0.5°C, even when the refrigeration system remained unstable.
Mestrado
Sistemas de Processos Quimicos e Informatica
Mestre em Engenharia Química
許建平 and Kin-ping Hui. "Computer texture boundary detection based on texton model and neural positive feedback." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1994. http://hub.hku.hk/bib/B42574298.
Повний текст джерелаHui, Kin-ping. "Computer texture boundary detection based on texton model and neural positive feedback." [Hong Kong] : The University of Hong Kong, 1994. http://sunzi.lib.hku.hk/hkuto/record/B42574298.
Повний текст джерелаIngram, Stephen D. "Visual Feedback Stabilisation of a Cart Inverted Pendulum A." Thesis, University of Bradford, 2016. http://hdl.handle.net/10454/17375.
Повний текст джерелаGoodrich Engine Control Systems and Balfour Beatty Rail Technologies
Böhm, Urs Lucas. "Physiological inputs to cerebrospinal fluid-contacting neurons." Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066196/document.
Повний текст джерелаCerebrospinal fluid-contacting neurons (CSF-cNs) are ciliated cells surrounding the central canal. These cells are GABAergic, extend a brush of microvilli into the lumen and are specified by the expression of the transient receptor potential ion channel Pkd2l1. The atypical morphology of CSF-cNs and their location make them candidates for sensory cells. It has been shown that CSF-cNs modulate locomotion by projecting onto the locomotor central pattern generators (CPGs) and that CSF-cNs can react to changes of pH in vitro, but the sensory modality these cells convey to spinal circuits and their relevance to locomotion remain elusive. In my thesis I investigate the sensory function of CSF-cNs in the zebrafish larva spinal cord. By combining proton uncaging together with pH imaging and calcium imaging, we could show that CSF-cNs respond to pulses of acidification in vivo and that this response persists in pkd2l1 mutants. Using genetically encoded calcium sensors we showed that CSF-cNs are not coordinately activated during fictive locomotion. Active or passive tail movement, however, led to CSF-cN activation restrained to cells ipsilateral to muscle contraction. These observations suggest that CSF-cNs are recruited by ipsilateral muscle contraction and/or tail torsion. Pkd2l1 mutants showed a decreased response to active and passive bending of the tail and a subtle but consistent decrease of tail-beat frequency was observed in the startle response. Altogether, the presented work shows evidence that CSF-cNs respond to changes in CSF pH and reveals that CSF-cNs constitute a mechanosensory organ which operates during locomotion to modulate spinal CPGs
Deng, Jiamei. "Predictive control of nonlinear systems using feedback linearisation based on dynamic neural networks." Thesis, University of Reading, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.433463.
Повний текст джерелаBereczki, Márk. "Graph Neural Networks for Article Recommendation based on Implicit User Feedback and Content." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300092.
Повний текст джерелаRekommendationssystem används ofta på webbplatser och applikationer för att hjälpa användare att hitta relevant innehåll baserad på deras intressen. Med utvecklingen av grafneurala nätverk nådde toppmoderna resultat inom rekommendationssystem och representerade data i form av en graf. De flesta grafbaserade lösningar har dock svårt med beräkningskomplexitet eller att generalisera till nya användare. Därför föreslår vi ett nytt grafbaserat rekommendatorsystem genom att modifiera Simple Graph Convolution. De här tillvägagångssätt är en effektiv grafnodsklassificering och lägga till möjligheten att generalisera till nya användare. Vi bygger vårt föreslagna rekommendatorsystem för att rekommendera artiklarna från Peltarion Knowledge Center. Genom att integrera två datakällor, implicit användaråterkoppling baserad på sidvisningsdata samt innehållet i artiklar, föreslår vi en hybridrekommendatörslösning. Under våra experiment jämför vi vår föreslagna lösning med en matrisfaktoriseringsmetod samt en popularitetsbaserad och en slumpmässig baslinje, analyserar hyperparametrarna i vår modell och undersöker förmågan hos vår lösning att ge rekommendationer till nya användare som inte deltog av träningsdatamängden. Vår modell resulterar i något mindre men liknande Mean Average Precision och Mean Reciprocal Rank poäng till matrisfaktoriseringsmetoden och överträffar de popularitetsbaserade och slumpmässiga baslinjerna. De viktigaste fördelarna med vår modell är beräkningseffektivitet och dess förmåga att ge relevanta rekommendationer till nya användare utan behov av omskolning av modellen, vilket är nyckelfunktioner för verkliga användningsfall.
Nardi, Flavio. "Neural network based adaptive alogrithms for nonlinear control." Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/12012.
Повний текст джерелаBoitard, Constance. "Identification des réseaux neurobiologiques gouvernant les apprentissages ambigus chez l'abeille Apis mellifera." Thesis, Toulouse 3, 2015. http://www.theses.fr/2015TOU30125/document.
Повний текст джерелаAssociative learning spans different levels of complexity, from simple tasks involving simple causal relationships between events, to ambiguous tasks, in which animals have to solve complex discriminations based on non-linear associative links. We focused on two protocols presenting a temporal or configural ambiguity at the level of stimulus contingencies in honey bees (\textit{Apis mellifera}). We performed selective blockades of GABAergic signalisation from recurrent feedback neurons in the mushroom bodies (MBs), higher-order insect brain structures associated with memory storage and retrieval, and found that this blockade within the MB calyces impaired both ambiguous learning tasks, although if did not affect simple conditioning counterparts. We suggest that the A3v cluster of the GABA feedback neurons innervating the MBs calyces are thus dispensable for simple learning, but are required for counteracting stimulus ambiguity in complex discriminations in honey bees
Zhong, Junpei [Verfasser], and Stefan [Akademischer Betreuer] Wermter. "Artificial Neural Models for Feedback Pathways for Sensorimotor Integration / Junpei Zhong. Betreuer: Stefan Wermter." Hamburg : Staats- und Universitätsbibliothek Hamburg, 2015. http://d-nb.info/1073248208/34.
Повний текст джерелаPérez, Dattari Rodrigo Javier. "Interactive learning with corrective feedback for continuous-action policies based on deep neural networks." Tesis, Universidad de Chile, 2019. http://repositorio.uchile.cl/handle/2250/170535.
Повний текст джерелаMemoria para optar al título de Ingeniero Civil Eléctrico
El Aprendizaje Reforzado Profundo (DRL) se ha transformado en una metodología poderosa para resolver problemas complejos de toma de decisión secuencial. Sin embargo, el DRL tiene varias limitaciones cuando es usado en problemas del mundo real (p.ej. aplicaciones de robótica). Por ejemplo, largos tiempos de entrenamiento (que no se pueden acelerar) son requeridos, en contraste con ambientes simulados, y las funciones de recompensa pueden ser difíciles de especificar/modelar y/o computar. Más aún, el traspaso de políticas aprendidas en simulaciones al mundo real no es directo (\emph{reality gap}). Por otro lado, métodos de aprendizaje de máquinas basados en la transferencia de conocimiento humano a un agente han mostrado ser capaces de obtener políticas con buenos desempeños sin necesariamente requerir el uso de una función de recompensa, siendo eficientes en lo que respecta al tiempo. En este contexto, en esta tesis se introduce una estrategia de Aprendizaje Interactivo de Máquinas (IML) para entrenar políticas modeladas como Redes Neuronales Profundas (DNNs), basada en retroalimentación correctiva humana con un método llamado D-COACH. Se combina Aprendizaje Profundo (DL) con el método Asesoramiento Correctivo Comunicado por Humanos (COACH), en donde humanos no expertos pueden entrenar políticas corrigiendo las acciones que va tomando el agente en ejecución. El método D-COACH tiene el potencial de resolver problemas complejos sin la necesidad de utilizar muchos datos o tiempo. Resultados experimentales validan la eficiencia del método propuesto en plataformas simuladas y del mundo real, en espacios de estados de baja y alta dimensionalidad, mostrando la capacidad de aprender políticas en espacios de acción continuos de manera efectiva. El método propuesto mostró resultados particularmente interesantes cuando políticas parametrizadas con Redes Neuronales Convolucionales (CNNs) fueron usadas para resolver problemas con espacios de estado de alta dimensionalidad, como pixeles desde una imagen. Al usar CNNs, los agentes tienen la capacidad de construir valiosas representaciones del estado del ambiente sin la necesidad de hacer ingeniería de características por el lado del diseñador (lo que era siempre necesario en el Aprendizaje Reforzado (RL) clásico). Estas propiedades pueden ser muy útiles en robótica, ya que es común encontrar aplicaciones en donde la información adquirida por los sensores del sistema es de alta dimensionalidad, como imágenes RGB. Darles la habilidad a los robots de aprender desde datos del alta dimensionalidad va a permitir aumentar la complejidad de los problemas que estos pueden resolver. A lo largo de esta tesis se proponen y validan tres variaciones de D-COACH. La primera introduce una estructura general para resolver problemas de estado de baja y alta dimensionalidad. La segunda propone una variación del primer método propuesto para problemas de estado de alta dimensionalidad, reduciendo el tiempo y esfuerzo de un humano al entrenar una política. Y por último, la tercera introduce el uso de Redes Neuronales Recurrentes para añadirle memoria a los agentes en problemas con observabilidad parcial.
FONDECYT 1161500
Sdrulla, Dan Alexandru. "Adenosine-dependent short- and long-term changes in hippocampal synaptic plasticity /." Connect to full text via ProQuest. IP filtered, 2005.
Знайти повний текст джерелаTypescript. Includes bibliographical references (leaves 96-111). Free to UCDHSC affiliates. Online version available via ProQuest Digital Dissertations;
Tan, Daniel. "Restoring Sensation in Human Upper Extremity Amputees using Chronic Peripheral Nerve Interfaces." Case Western Reserve University School of Graduate Studies / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=case1405070015.
Повний текст джерелаGuglielmi, Luca [Verfasser], and Matthias [Akademischer Betreuer] Carl. "A Wif1 mediated feedback loop suppresses premature Wnt signaling in nascent habenular neurons / Luca Guglielmi ; Betreuer: Matthias Carl." Heidelberg : Universitätsbibliothek Heidelberg, 2018. http://d-nb.info/1177385058/34.
Повний текст джерелаDu, Hongliu. "Control of systems with uncertainties /." free to MU campus, to others for purchase, 1997. http://wwwlib.umi.com/cr/mo/fullcit?p9841139.
Повний текст джерелаBarrett, Andrea Lynn. "A FGF-Hh feedback loop controls stem cell proliferation in the developing larval brain of drosophila melanogaster." [College Station, Tex. : Texas A&M University, 2007. http://hdl.handle.net/1969.1/ETD-TAMU-2017.
Повний текст джерелаKim, Nakwan. "Improved Methods in Neural Network-Based Adaptive Output Feedback Control, with Applications to Flight Control." Diss., Georgia Institute of Technology, 2003. http://hdl.handle.net/1853/5282.
Повний текст джерелаGibb, Leif. "Inhibition, recurrent excitation, and neural feedback in computational models of sparse bursting and birdsong sequencing." Diss., [La Jolla, Calif.] : University of California, San Diego, 2009. http://wwwlib.umi.com/cr/ucsd/fullcit?p3344677.
Повний текст джерелаTitle from first page of PDF file (viewed March 19, 2009). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references.
Trujillo, Logan Thomas. "Electrophysiological Correlates of the Influences of Past Experience on Conscious and Unconscious Figure-Ground Perception." Diss., The University of Arizona, 2007. http://hdl.handle.net/10150/194981.
Повний текст джерелаEyng, Eduardo. "Controle feedforward-feedback aplicado as colunas de absorção do processo de produção de etanol por fermentação." [s.n.], 2008. http://repositorio.unicamp.br/jspui/handle/REPOSIP/267254.
Повний текст джерелаTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Quimica
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Resumo: O etanol perdido por evaporacao durante o processo de producao por fermentação pode ser recuperado por uma coluna de absorcao, a qual requer um sistema de controle robusto de acordo com sua complexidade. Este equipamento tambem e utilizado no tratamento do CO2, um co-produto deste processo. Neste sentido, no presente trabalho foi proposto e testado, o emprego de controladores feedforward-feedback, baseados em modelo inverso de redes neurais, os quais manipulam as vazoes de solvente que sao alimentados as colunas, para controlar a concentracao de etanol na corrente gasosa a saida da primeira coluna, e a concentracao de agua residual no efluente gasoso da segunda. Simulacoes foram feitas, sendo abordado tanto o problema regulatorio quanto o problema servo para as duas colunas de absorcao do processo estudado. O desempenho do controlador neural foi superior ao apresentado por um controlador convencional PID, para ambas as colunas de absorcao estudadas, pois o tempo de resposta, assim como o overshoot foi menor. A superioridade do controlador neural foi comprovada pelos valores obtidos para os parametros ITAE (integral do erro absoluto ponderada pelo tempo), IAE (integral do erro absoluto) e ISE (integral do quadrado do erro). Outro objetivo deste trabalho foi avaliar a influencia das incertezas nos sensores sobre o desempenho do sistema de controle. Foram testados tres niveis: 5, 10 e 15%, sendo as incertezas inseridas nas variaveis de entrada do tipo concentracao de etanol/agua residual na corrente gasosa. Para a coluna de recuperacao de etanol, tanto para o problema regulatorio quanto servo, nenhum controlador conseguiu estabilizar a variavel controlada no set point, no entanto, quando empregado o controlador neural a amplitude da faixa de oscilacao da variavel controlada foi menor para todos os niveis de incerteza testados. Já para a coluna de tratamento de CO2, os controladores encontraram dificuldades em manter a estabilidade do sistema. Neste sentido, o controlador neural apresentou um desempenho satisfatorio para incertezas de 5 e 10%, enquanto que o PID nao conseguiu manter o sistema estavel para incertezas superiores a 5%. Com base nos testes realizados foi constatado que o controlador neural proposto constitui uma opcao atrativa para o controle das colunas de absorcao do processo deproducao de etanol por fermentacao, principalmente quando os niveis de incerteza forem de ate 10%.
Abstract: Some of ethanol lost by evaporation during its fermentation production process may be recovered using an absorption column, which requires a robust control system. This equipment also is used on carbonic gas treatment, a by-product of this process. In the present work, the development of nonlinear feedforward-feedback controllers, based on a neural network inverse model, was proposed and tested to manipulate the absorbent flow rates in order to control the residual ethanol concentration in the effluent gas phase at the first absorption column, and the residual water at the second one. Simulation studies were carried out for the regulator and servo problem, for both absorption columns studied. The neural controller proposed outperformed a conventional PID, because the response time, and also the overshoot were smaller when the neural controller was applied. The results were confirmed by the ITAE (integral of time multiplied by the absolute error), IAE (integral of absolute error) and ISE (integral of square error) parameters. The measurement uncertainties influence on control system performance was tested for three levels: 5, 10 and 15%. The uncertainties were introduced on ethanol/residual water concentration on gas phase. For the ethanol recovery column, neither PID nor the neural controller drove the controlled variable exactly to the set point, however, the neural controller provided a smaller oscillation for all uncertainty levels tested, for regulator and servo problem. The neural controller also outperformed PID in CO2 treatment column. For the regulator and servo problems the neural controller successfully proceeded when the uncertainty level was 5% or 10%, while the PID did not deal adequately with uncertainties above 5%. Therefore, the proposed neural controller proved be an attractive control solution for the absorption columns of ethanol production process by fermentation, especially when the input variables carry small uncertainties ( less than 10%) from the sensors.
Doutorado
Sistemas de Processos Quimicos e Informatica
Doutor em Engenharia Química
Williams, Carrie. "Influence of Sensory Feedback on Rhythmic Movement: A Computational Study of Resonance Tuning in Biological Systems." Diss., Available online, Georgia Institute of Technology, 2006, 2006. http://etd.gatech.edu/theses/available/etd-11172006-180642/.
Повний текст джерелаDeWeerth, Stephen, Committee Chair ; Lee, Robert, Committee Member ; Ting, Lena, Committee Member ; Katz, Paul, Committee Member ; Butera, Robert, Committee Member.
Vance, Jonathan Blake. "Neural network control of nonstrict feedback and nonaffine nonlinear discrete-time systems with application to engine control." Diss., Rolla, Mo. : University of Missouri-Rolla, 2007. http://scholarsmine.umr.edu/thesis/pdf/Vance_09007dcc8043fb11.pdf.
Повний текст джерелаVita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed March 26, 2008) Includes bibliographical references.
Thurston, Peter William. "Sequentiality in artificial neural networks : an account of three models exploring the benefit of time delayed feedback." Thesis, University of Kent, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.257204.
Повний текст джерелаQuérée, Philip. "Feedback control in the central 5-HT system : evidence for a role of 5-HT₂c receptors." Thesis, University of Oxford, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.670061.
Повний текст джерелаGong, Jianwei. "NON-SILICON MICROFABRICATED NANOSTRUCTURED CHEMICAL SENSORS FOR ELECTRIC NOSE APPLICATION." Doctoral diss., University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4082.
Повний текст джерелаPh.D.
Department of Mechanical, Materials and Aerospace Engineering;
Engineering and Computer Science
Mechanical Engineering
Klinke, Ina [Verfasser]. "Associative plasticity and context modulation in GABAergic feedback neurons of the mushroom body output in the honeybee (Apis mellifera) / Ina Klinke." Berlin : Freie Universität Berlin, 2012. http://d-nb.info/1026992354/34.
Повний текст джерелаShih, Peter. "Reinforcement-learning based output-feedback controller for nonlinear discrete-time system with application to spark ignition engines operating lean and EGR." Diss., Rolla, Mo. : University of Missouri-Rolla, 2007. http://scholarsmine.umr.edu/thesis/pdf/Shih_thesis_2007_030207_09007dcc80336990.pdf.
Повний текст джерелаVita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed May 16, 2007) Includes bibliographical references.
Fidelin, Kevin. "Modulation of premotor circuits controlling locomotor activity by spinal GABAergic sensory neurons in zebrafish : connectivity mapping of an intraspinal sensory feedback circuit." Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066200/document.
Повний текст джерелаUnderstanding how the central nervous system generates motor sequences, coordinates limbs and body orientation in an ever-changing environment, while adapting to sensory cues remains a central question in the field of systems neuroscience. The work presented here aims to understand how local sensory neurons in the spinal cord contribute to the production and/ or the modulation of locomotor activity. We focused our work on a conserved class of spinal sensory neurons termed cerebrospinal fluid contacting neurons (CSF-cNs). These neurons lie at the interface between the CSF and spinal interneurons controlling motor output and represent an interesting yet poorly understood sensorimotor loop in the vertebrate spinal cord. However, the connectivity of CSF-cNs remains completely uncharacterized. To understand how CSF-cNs modulate locomotion in vertebrates, we combined genetics, imaging, optogenetics, electrophysiology, and behavior analysis to map the functional connectivity of these sensory neurons and test their function in the zebrafish larva. Our results demonstrate that CSF-cNs target several elements thought to be part of the locomotor central pattern generator in zebrafish, including glutamatergic spinal neurons involved in slow and fast swimming. We show that CSF-cNs can modulate the duration and occurrence of spontaneous locomotor events in a state dependent manner and tune the frequency of evoked fast escape responses. Altogether our work dissecting sensorimotor integration in the spinal cord bridged single cell function in vivo to behavior in zebrafish and should contribute to a better understanding of the role of sensory feedback during locomotion in vertebrates