subject:(neuro controller)

1. Balaji, K. Second order KY converter voltage ripple reduction and load regulation by adaptive neuro fuzzy controller; -.

Degree: Electronic engineering, 2015, St Peters University

URL: http://shodhganga.inflibnet.ac.in/handle/10603/42703

Available

Bibliography p.96-105

Advisors/Committee Members: Manic, Suresh K.

Subjects/Keywords: adaptive neuro fuzzy controller; KY converter

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APA (6^th Edition):

Balaji, K. (2015). Second order KY converter voltage ripple reduction and load regulation by adaptive neuro fuzzy controller; -. (Thesis). St Peters University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/42703

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Balaji, K. “Second order KY converter voltage ripple reduction and load regulation by adaptive neuro fuzzy controller; -.” 2015. Thesis, St Peters University. Accessed February 27, 2021. http://shodhganga.inflibnet.ac.in/handle/10603/42703.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Balaji, K. “Second order KY converter voltage ripple reduction and load regulation by adaptive neuro fuzzy controller; -.” 2015. Web. 27 Feb 2021.

Vancouver:

Balaji K. Second order KY converter voltage ripple reduction and load regulation by adaptive neuro fuzzy controller; -. [Internet] [Thesis]. St Peters University; 2015. [cited 2021 Feb 27]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/42703.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Balaji K. Second order KY converter voltage ripple reduction and load regulation by adaptive neuro fuzzy controller; -. [Thesis]. St Peters University; 2015. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/42703

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Vrije Universiteit Amsterdam

2. Nitschke, G.S. Neuro-Evolution for Emergent Specialization in Collective Behavior Systems .

Degree: 2009, Vrije Universiteit Amsterdam

URL: http://hdl.handle.net/1871/13153

Subjects/Keywords: Controller design using neuro-evolution in collective behaviour systems; Neuro-Evolution; Collective Behavior

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APA (6^th Edition):

Nitschke, G. S. (2009). Neuro-Evolution for Emergent Specialization in Collective Behavior Systems . (Doctoral Dissertation). Vrije Universiteit Amsterdam. Retrieved from http://hdl.handle.net/1871/13153

Chicago Manual of Style (16^th Edition):

Nitschke, G S. “Neuro-Evolution for Emergent Specialization in Collective Behavior Systems .” 2009. Doctoral Dissertation, Vrije Universiteit Amsterdam. Accessed February 27, 2021. http://hdl.handle.net/1871/13153.

MLA Handbook (7^th Edition):

Nitschke, G S. “Neuro-Evolution for Emergent Specialization in Collective Behavior Systems .” 2009. Web. 27 Feb 2021.

Vancouver:

Nitschke GS. Neuro-Evolution for Emergent Specialization in Collective Behavior Systems . [Internet] [Doctoral dissertation]. Vrije Universiteit Amsterdam; 2009. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/1871/13153.

Council of Science Editors:

Nitschke GS. Neuro-Evolution for Emergent Specialization in Collective Behavior Systems . [Doctoral Dissertation]. Vrije Universiteit Amsterdam; 2009. Available from: http://hdl.handle.net/1871/13153

University of Michigan

3. Johri, Rajit. Neuro_Dynamic Programming and Reinforcement Learning for Optimal Energy Management of a Series Hydraulic Hybrid Vehicle Considering Engine Transient Emissions.

Degree: PhD, Mechanical Engineering, 2011, University of Michigan

URL: http://hdl.handle.net/2027.42/89829

► Sequential decision problems under uncertainty are encountered in various fields such as optimal control and operations research. In this dissertation, Neuro-Dynamic Programming (NDP) and Reinforcement… (more)

▼ Sequential decision problems under uncertainty are encountered in various fields such as optimal control and operations research. In this dissertation, Neuro-Dynamic Programming (NDP) and Reinforcement Learning (RL) are applied to address policy optimization problems with multiple objectives and large design state space. Dynamic Programming (DP) is well suited for determining an optimal solution for constrained nonlinear model based systems. However, DP suffers from curse of dimensionality i.e. computational effort grows exponentially with state space. The new algorithms address this problem and enable practical application of DP to a much broader range of problems. The other contribution is to design fast and computationally efficient transient emission models. The power management problem for a hybrid vehicle can be formulated as an infinite time horizon stochastic sequential decision-making problem. In the past, policy optimization has been applied successfully to design optimal supervisory controller for best fuel economy. Static emissions have been considered too but engine research has shown that transient operation can have significant impact on real-world emissions. Modeling transient emissions results in addition of more states. Therefore, the problem with multiple objectives i.e. minimize fuel consumption and transient particulate and NOX emissions, becomes computationally intractable by DP. This research captures the insight with models and brings it into the supervisory controller design. A self-learning supervisory controller is designed based on the principles of NDP and RL. The controller starts “naïve” i.e. with no knowledge to control the onboard power but learns to do so in an optimal manner after interacting with the system. The controller tries to minimize multiple objectives and continues to evolve until a global solution is achieved. Virtual sensors for predicting real-time transient particulate and NOX emissions are developed using neuro-fuzzy modeling technique, which utilizes a divide-and-conquer strategy. The highly nonlinear engine operating space is partitioned into smaller subspaces and a separate local model is trained to for each subspace. Finally, the supervisory controller along with virtual emission sensors is implemented and evaluated using the Engine-In-the-Loop (EIL) setup. EIL is a unique facility to systematically evaluate control methodologies through concurrent running of real engine and a virtual hybrid powertrain. Advisors/Committee Members: Filipi, Zoran S. (committee member), Del Vecchio, Domittilla (committee member), Grizzle, Jessy W. (committee member), Hoard, John W. (committee member), Peng, Huei (committee member).

Subjects/Keywords: Neuro Dynamic Programming; Hybrid Vehicle; Supervisory Controller / Power Management; Soot and NOx Model; Optimal Control; Transient Diesel Emission; Mechanical Engineering; Engineering

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APA (6^th Edition):

Johri, R. (2011). Neuro_Dynamic Programming and Reinforcement Learning for Optimal Energy Management of a Series Hydraulic Hybrid Vehicle Considering Engine Transient Emissions. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/89829

Chicago Manual of Style (16^th Edition):

Johri, Rajit. “Neuro_Dynamic Programming and Reinforcement Learning for Optimal Energy Management of a Series Hydraulic Hybrid Vehicle Considering Engine Transient Emissions.” 2011. Doctoral Dissertation, University of Michigan. Accessed February 27, 2021. http://hdl.handle.net/2027.42/89829.

MLA Handbook (7^th Edition):

Johri, Rajit. “Neuro_Dynamic Programming and Reinforcement Learning for Optimal Energy Management of a Series Hydraulic Hybrid Vehicle Considering Engine Transient Emissions.” 2011. Web. 27 Feb 2021.

Vancouver:

Johri R. Neuro_Dynamic Programming and Reinforcement Learning for Optimal Energy Management of a Series Hydraulic Hybrid Vehicle Considering Engine Transient Emissions. [Internet] [Doctoral dissertation]. University of Michigan; 2011. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/2027.42/89829.

Council of Science Editors:

Johri R. Neuro_Dynamic Programming and Reinforcement Learning for Optimal Energy Management of a Series Hydraulic Hybrid Vehicle Considering Engine Transient Emissions. [Doctoral Dissertation]. University of Michigan; 2011. Available from: http://hdl.handle.net/2027.42/89829

University of Manitoba

4. Aldaba, Cassandra. Investigation of virtual reality locomotion technology effects on simulator sickness and application for neuro-cogntive training for participants with memory problems.

Degree: Biomedical Engineering, 2018, University of Manitoba

URL: http://hdl.handle.net/1993/33301

► This thesis investigated the effects of virtual reality locomotive controllers with a head-mounted display on a user’s simulator sickness and controller’s intuitiveness. The investigation included… (more)

▼ This thesis investigated the effects of virtual reality locomotive controllers with a head-mounted display on a user’s simulator sickness and controller’s intuitiveness. The investigation included two types of controllers: seated static methods (TiltChair and game pad joystick) and mobile methods (omni-directional treadmill and our manual wheelchair joystick, VRNChair). Initially, young adult participants selected the TiltChair that caused the most severe simulator sickness due to its asynchronous rotation between the participants’ necks and torsos. Thus, a second experiment investigated the effect of synchronous neck rotation via utilizing the TiltChair with a neck brace. Overall, participants that spent more time in virtual reality had significantly higher simulator sickness. Additionally, the TiltChair and joystick had the highest level of intuitiveness and the omni-directional treadmill had the lowest level of intuitiveness among the young adult participants. Since we recruited young adult participants, researchers should design virtual reality experiments based on their sample population’s susceptibility to simulator sickness and ability to utilize the virtual reality technology. Therefore, elderly participants with minimal computer experience would find the naturalistic motion of the VRNChair intuitive with minimal simulator sickness. To further investigate the feasibility of the virtual reality locomotive controllers, the thesis investigated participants with memory problems utilizing the VRNChair or TiltChair during a neuro-cognitive rehabilitation program. The participants had no difficulties with the virtual reality locomotive controller and neuro-cognitive training shopping task, but the participants did not have any significant memory improvements. Therefore, the virtual reality neuro-cognitive tasks should be re-designed to minimize simulator sickness and to optimize mental stimulation for any potential cognitive or memory improvements. Advisors/Committee Members: Moussavi, Zahra (Biomedical Engineering) (supervisor), Unger, Bertram J. (Internal Medicine) McLeod, Bob (Electrical and Computer Engineering) Eskicioglu, Rasit (Computer Science) (examiningcommittee).

Subjects/Keywords: Simulator Sickness; Virtual Reality; Neuro-cognitive training; VR; HMD; VRN Chair; Spatial Navigation; Cognitive Training; Locomotion Controller; Memory

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APA (6^th Edition):

Aldaba, C. (2018). Investigation of virtual reality locomotion technology effects on simulator sickness and application for neuro-cogntive training for participants with memory problems. (Masters Thesis). University of Manitoba. Retrieved from http://hdl.handle.net/1993/33301

Chicago Manual of Style (16^th Edition):

Aldaba, Cassandra. “Investigation of virtual reality locomotion technology effects on simulator sickness and application for neuro-cogntive training for participants with memory problems.” 2018. Masters Thesis, University of Manitoba. Accessed February 27, 2021. http://hdl.handle.net/1993/33301.

MLA Handbook (7^th Edition):

Aldaba, Cassandra. “Investigation of virtual reality locomotion technology effects on simulator sickness and application for neuro-cogntive training for participants with memory problems.” 2018. Web. 27 Feb 2021.

Vancouver:

Aldaba C. Investigation of virtual reality locomotion technology effects on simulator sickness and application for neuro-cogntive training for participants with memory problems. [Internet] [Masters thesis]. University of Manitoba; 2018. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/1993/33301.

Council of Science Editors:

Aldaba C. Investigation of virtual reality locomotion technology effects on simulator sickness and application for neuro-cogntive training for participants with memory problems. [Masters Thesis]. University of Manitoba; 2018. Available from: http://hdl.handle.net/1993/33301

University of Ontario Institute of Technology

5. Multani, Munish. Design and Optimization of Intelligent PI Controllers (Fuzzy and Neuro-Fuzzy) for HVDC Transmission System.

Degree: 2010, University of Ontario Institute of Technology

URL: http://hdl.handle.net/10155/103

► This thesis deals with enhancing the performance of Fuzzy Logic (FL) based PI controllers for High Voltage Direct Current Transmission Systems (HVDC) by optimizing the… (more)

Subjects/Keywords: HVDC converter; Fuzzy logic controller; Membership function; Neuro-fuzzy controller; EMTP-RV

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APA (6^th Edition):

Multani, M. (2010). Design and Optimization of Intelligent PI Controllers (Fuzzy and Neuro-Fuzzy) for HVDC Transmission System. (Thesis). University of Ontario Institute of Technology. Retrieved from http://hdl.handle.net/10155/103

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Multani, Munish. “Design and Optimization of Intelligent PI Controllers (Fuzzy and Neuro-Fuzzy) for HVDC Transmission System.” 2010. Thesis, University of Ontario Institute of Technology. Accessed February 27, 2021. http://hdl.handle.net/10155/103.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Multani, Munish. “Design and Optimization of Intelligent PI Controllers (Fuzzy and Neuro-Fuzzy) for HVDC Transmission System.” 2010. Web. 27 Feb 2021.

Vancouver:

Multani M. Design and Optimization of Intelligent PI Controllers (Fuzzy and Neuro-Fuzzy) for HVDC Transmission System. [Internet] [Thesis]. University of Ontario Institute of Technology; 2010. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/10155/103.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Multani M. Design and Optimization of Intelligent PI Controllers (Fuzzy and Neuro-Fuzzy) for HVDC Transmission System. [Thesis]. University of Ontario Institute of Technology; 2010. Available from: http://hdl.handle.net/10155/103

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Pontifical Catholic University of Rio de Janeiro

6. VINICIUS DE MELLO LIMA. [en] OBSTACLE DETECTION AND AVOIDANCE SYSTEM FOR UAV S, BASED ON NEURO-FUZZY CONTROLLER.

Degree: 2019, Pontifical Catholic University of Rio de Janeiro

URL: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=37757

►

[pt] Esta dissertação apresenta o projeto e desenvolvimento de um sistema para detecção e desvio de obstáculos para veículos aéreos não tripulados (VANTs), implementado por… (more)

▼

[pt] Esta dissertação apresenta o projeto e desenvolvimento de um sistema para detecção e desvio de obstáculos para veículos aéreos não tripulados (VANTs), implementado por um controlador neuro-fuzzy. Neste contexto, este trabalho apresenta uma revisão teórica sobre veículos aéreos não tripuláveis, legislação brasileira aplicável, métodos de detecção de obstáculos, lógica nebulosa e redes neurais. O controlador desenvolvido foi implementado de forma a imitar as ações realizadas por um operador humano, visando desviar de obstáculos encontrados no caminho de navegação do VANT. Regras de inferência são estabelecidas baseadas na consultoria de especialistas da área e os pesos ajustados pela rede neural. O processo de tomada de decisão ocorre levando em consideração as informações coletadas por um Lidar multicanal e sensores ultrassônicos embarcados no VANT. Por sua vez, o algoritmo desenvolvido foi incorporado em um controlador de vôo comercial. O sistema completo do quadricóptero é detalhado, destacando as principais características de todos os sensores e do controlador de vôo. Os resultados das simulações computacionais e testes experimentais são apresentados, discutidos e comparados, a fim de avaliar o desempenho do sistema desenvolvido.

[en] This dissertation presents the design and development of an obstacle detection and avoidance system for unmanned aerial vehicles, implemented by a neuro-fuzzy controller. In this context, this work presents a theoretical review of unmanned aerial vehicles, the applicable Brazilian legislation, obstacle detection methods, fuzzy logic and neural networks. The developed controller was implemented in order to mimic the actions taken by a human operator, aiming at avoiding obstacles found in the navigation path of the UAV. Inference rules were established based on consultation with specialists in the field and the weights adjusted by neural networks. The decisionmaking process takes into account information collected by a multichannel Lidar and ultrasonic sensors embedded in the UAV. In turn, the developed algorithm was embedded in a commercial flight controller. The complete quadricopter system is detailed, highlighting the key features of all sensors and the flight controller. The results of computational simulations and experimental tests are presented, discussed and compared, in order to evaluate the performance of the developed system.

Advisors/Committee Members: EDUARDO COSTA DA SILVA, EDUARDO COSTA DA SILVA, EDUARDO COSTA DA SILVA.

Subjects/Keywords: [pt] SONAR; [en] SONAR; [pt] VANT; [en] UAV; [pt] ENGENHARIA ELETRICA - TESES; [en] ELECTRICAL ENGINEERING - THESIS; [pt] SISTEMA DE DETECCAO E DESVIO DE OBSTACULO; [en] OBSTACLE DETECTION AND AVOIDANCE SYSTEM; [pt] CONTROLADOR NEURO-FUZZY; [en] NEURO-FUZZY CONTROLLER; [pt] LIDAR; [en] LIDAR

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APA (6^th Edition):

LIMA, V. D. M. (2019). [en] OBSTACLE DETECTION AND AVOIDANCE SYSTEM FOR UAV S, BASED ON NEURO-FUZZY CONTROLLER. (Thesis). Pontifical Catholic University of Rio de Janeiro. Retrieved from http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=37757

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

LIMA, VINICIUS DE MELLO. “[en] OBSTACLE DETECTION AND AVOIDANCE SYSTEM FOR UAV S, BASED ON NEURO-FUZZY CONTROLLER.” 2019. Thesis, Pontifical Catholic University of Rio de Janeiro. Accessed February 27, 2021. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=37757.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

LIMA, VINICIUS DE MELLO. “[en] OBSTACLE DETECTION AND AVOIDANCE SYSTEM FOR UAV S, BASED ON NEURO-FUZZY CONTROLLER.” 2019. Web. 27 Feb 2021.

Vancouver:

LIMA VDM. [en] OBSTACLE DETECTION AND AVOIDANCE SYSTEM FOR UAV S, BASED ON NEURO-FUZZY CONTROLLER. [Internet] [Thesis]. Pontifical Catholic University of Rio de Janeiro; 2019. [cited 2021 Feb 27]. Available from: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=37757.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

LIMA VDM. [en] OBSTACLE DETECTION AND AVOIDANCE SYSTEM FOR UAV S, BASED ON NEURO-FUZZY CONTROLLER. [Thesis]. Pontifical Catholic University of Rio de Janeiro; 2019. Available from: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=37757

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Colorado State University

7. Kretchmar, R. Matthew. Synthesis of reinforcement learning and robust control theory, A.

Degree: PhD, Computer Science, 2000, Colorado State University

URL: http://hdl.handle.net/10217/26305

► The pursuit of control algorithms with improved performance drives the entire control research community as well as large parts of the mathematics, engineering, and artificial… (more)

▼ The pursuit of control algorithms with improved performance drives the entire control research community as well as large parts of the mathematics, engineering, and artificial intelligence research communities. A fundamental limitation on achieving control performance is the conflicting requirement of maintaining system stability. In general, the more aggressive is the controller, the better the control performance but also the closer to system instability. Robust control is a collection of theories, techniques, the tools that form one of the leading edge approaches to control. Most controllers are designed not on the physical plant to be controlled, but on a mathematical model of the plant; hence, these controllers often do not perform well on the physical plant and are sometimes unstable. Robust control overcomes this problem by adding uncertainty to the mathematical model. The result is a more general, less aggressive controller which performs well on the both the model and the physical plant. However, the robust control method also sacrifices some control performance in order to achieve its guarantees of stability. Reinforcement learning based neural networks offer some distinct advantages for improving control performance. Their nonlinearity enables the neural network to implement a wider range of control functions, and their adaptability permits them to improve control performance via on-line, trial-and-error learning. However, neuro-control is typically plagued by a lack of stability guarantees. Even momentary instability cannot be tolerated in most physical plants, and thus, the threat of instability prohibits the application of neuro-control in many situations. In this dissertation, we develop a stable neuro-control scheme by synthesizing the two fields of reinforcement learning and robust control theory. We provide a learning system with many of the advantages of neuro-control. Using functional uncertainty to represent the nonlinear and time-varying components of the neuro networks, we apply the robust control techniques to guarantee the stability of our neuro-controller. Our scheme provides stable control not only for a specific fixed-weight, neural network, but also for a neuro-controller in which the weights are changing during learning. Furthermore, we apply our stable neuro-controller to several control tasks to demonstrate that the theoretical stability guarantee is readily applicable to real-life control situations. We also discuss several problems we encounter and identify potential avenues of future research. Advisors/Committee Members: Anderson, Charles (advisor), Howe, Adele E. (committee member), Whitley, L. Darrell (committee member), Young, Peter M. (committee member), Hittle, Douglas C. (committee member).

Subjects/Keywords: robust control theory; neuro-control scheme; neuro-controller; neural networks; Reinforcement learning; Control theory

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APA (6^th Edition):

Kretchmar, R. M. (2000). Synthesis of reinforcement learning and robust control theory, A. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/26305

Chicago Manual of Style (16^th Edition):

Kretchmar, R Matthew. “Synthesis of reinforcement learning and robust control theory, A.” 2000. Doctoral Dissertation, Colorado State University. Accessed February 27, 2021. http://hdl.handle.net/10217/26305.

MLA Handbook (7^th Edition):

Kretchmar, R Matthew. “Synthesis of reinforcement learning and robust control theory, A.” 2000. Web. 27 Feb 2021.

Vancouver:

Kretchmar RM. Synthesis of reinforcement learning and robust control theory, A. [Internet] [Doctoral dissertation]. Colorado State University; 2000. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/10217/26305.

Council of Science Editors:

Kretchmar RM. Synthesis of reinforcement learning and robust control theory, A. [Doctoral Dissertation]. Colorado State University; 2000. Available from: http://hdl.handle.net/10217/26305

Pontifical Catholic University of Rio de Janeiro

8. GIOVANE QUADRELLI. [en] NEURO-FUZZY MODELLING AND CONTROL OF DYNAMIC SISTEMS.

Degree: 2002, Pontifical Catholic University of Rio de Janeiro

URL: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=2701

►

[pt] Este trabalho apresenta procedimentos de modelagem e controle neuro-fuzzy de sistemas dinâmicos. Neste contexto, é proposta e avaliada a utilização simultânea da abordagem neuro-fuzzy… (more)

▼

[pt] Este trabalho apresenta procedimentos de modelagem e controle neuro-fuzzy de sistemas dinâmicos. Neste contexto, é proposta e avaliada a utilização simultânea da abordagem neuro-fuzzy em todo o sistema de malha fechada controlador-planta.Na modelagem da planta, o espaço de entrada do sistema dinâmico é inicialmente dividido em um número de regiões de operação fuzzy onde modelos de ordem reduzida (ARMAX) representam o comportamento do sistema dinâmico. A saída completa do sistema - modelo global - é obtida através da conjunção das saídas dos modelos locais usando uma rede neuro-fuzzy.No controle da planta, é proposto um novo controlador neuro-fuzzy chamado Controlador Neuro-fuzzy de Coeficientes Variáveis (CNFCV), que tem como objetivos melhorar a robustez do sistema de controle a perturbações e a geração automática da variável manipulada, que é uma dificuldade normalmente encontrada em controladores neurais ou neuro-fuzzy. Esse controlador é originado dos modelos de redes neurais de Mellem (1997) e Velloso (1999), e utiliza redes neuro-fuzzy para a geração dos coeficientes variáveis de um modelo ARMA da variável manipulada. Apesar de juntar modelos de séries temporais com a abordagem neuro-fuzzy, o CNFCV tem como função não a previsão, mas sim o controle de uma planta ou processo.Para avaliar o desempenho do CNFCV são utilizados, como meios de comparação,controladores neuro- fuzzy conhecidos - FALCON-H Fuzzy Adaptive Learning Control Network with Hybrid Learning e NEFCON Neuro-Fuzzy Controller - e o tradicional controlador PID Proporcional- Integral-Derivativo.As plantas utilizadas são uma planta linear Bobinador, uma planta linearizada Pêndulo Invertido e uma planta não linear %CO2. A escolha de tais plantas deve-se ao fato de serem utilizadas e modeladas em aplicações práticas e em trabalhos acadêmicos. Os resultados obtidos com o CNFCV são analisados e comparados aos proporcionados pelas outras estruturas.Ao final são apresentadas conclusões e sugestões para trabalhos futuros.

[en] In this work procedures for neuro-fuzzy modelling and control of dynamic systems are reviewed and a new structure is proposed. In this, modelling and closed-loop control are performed simultaneously by using a neuro-fuzzy approach. In the modelling stage the input space of a dynamic system (plant) is initially divided into a number of fuzzy operating regions within which reduced order models are able to represent the system. The complete system model output - the global model - is obtained through the conjunction of the outputs of the local models. A new structure, called Neuro-Fuzzy Controller with Variable Coefficients (NFCVC) is proposed and evaluated. Its main objectives are to improve the system s robustness and to provide automatic generation of the manipulated variable in order to overcome a difficulty of neural and neuro-fuzzy controllers in general. The NFCVC is originated from models proposed by Mellem (1997) and Velloso (1999) and makes use of neuro-fuzzy networks to generate variable coefficients of an ARMA model.…

Advisors/Committee Members: RICARDO TANSCHEIT, MARLEY MARIA BERNARDES REBUZZI VELLASCO.

Subjects/Keywords: [pt] REDES NEURAIS; [en] NEURAL NETWORKS; [pt] LOGICA FUZZY; [en] FUZZY LOGIC; [pt] CONTROLADOR NEURO-FUZZY DE COEFICIENTES VARIAVEIS; [en] NEURO-FUZZY CONTROLLER WITH VARIABLE COEFFICIENTS

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APA (6^th Edition):

QUADRELLI, G. (2002). [en] NEURO-FUZZY MODELLING AND CONTROL OF DYNAMIC SISTEMS. (Thesis). Pontifical Catholic University of Rio de Janeiro. Retrieved from http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=2701

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

QUADRELLI, GIOVANE. “[en] NEURO-FUZZY MODELLING AND CONTROL OF DYNAMIC SISTEMS.” 2002. Thesis, Pontifical Catholic University of Rio de Janeiro. Accessed February 27, 2021. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=2701.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

QUADRELLI, GIOVANE. “[en] NEURO-FUZZY MODELLING AND CONTROL OF DYNAMIC SISTEMS.” 2002. Web. 27 Feb 2021.

Vancouver:

QUADRELLI G. [en] NEURO-FUZZY MODELLING AND CONTROL OF DYNAMIC SISTEMS. [Internet] [Thesis]. Pontifical Catholic University of Rio de Janeiro; 2002. [cited 2021 Feb 27]. Available from: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=2701.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

QUADRELLI G. [en] NEURO-FUZZY MODELLING AND CONTROL OF DYNAMIC SISTEMS. [Thesis]. Pontifical Catholic University of Rio de Janeiro; 2002. Available from: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=2701

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

9. Ατσαλάκης, Γιώργος. Σύστημα πρόβλεψης της βραχυχρόνιας τάσης της τιμής των μετοχών με χρήση νευρο-ασαφών μεθόδων.

Degree: 2006, Technical University of Crete (TUC); Πολυτεχνείο Κρήτης

URL: http://hdl.handle.net/10442/hedi/14420

Subjects/Keywords: Πρόβλεψη αγορών; Πρόβλεψη δεικτών; Πρόβλεψη με νευρο-ασαφή συστήματα; Πρόβλεψη με νευρωνικά δίκτυα; Πρόβλεψη με ασαφή λογική; Χρηματιστήριο; Ελεγκτές για πρόβλεψη; Πρόβλεψη τιμών μετοχών; Πρόβλεψη χρηματαγορών; Πρόβλεψη τάσης μετοχών; Forecasting; Stock price forecasting; Stock exchange; Stock market; Neuro-fuzzy stock market forecasting; Neural network stock market forecasting; Fuzzy stock market forecasting; Forecasting by controller; Stock trend; Stock market trend

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APA (6^th Edition):

Ατσαλάκης, . �. (2006). Σύστημα πρόβλεψης της βραχυχρόνιας τάσης της τιμής των μετοχών με χρήση νευρο-ασαφών μεθόδων. (Thesis). Technical University of Crete (TUC); Πολυτεχνείο Κρήτης. Retrieved from http://hdl.handle.net/10442/hedi/14420

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Ατσαλάκης, Γιώργος. “Σύστημα πρόβλεψης της βραχυχρόνιας τάσης της τιμής των μετοχών με χρήση νευρο-ασαφών μεθόδων.” 2006. Thesis, Technical University of Crete (TUC); Πολυτεχνείο Κρήτης. Accessed February 27, 2021. http://hdl.handle.net/10442/hedi/14420.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Ατσαλάκης, Γιώργος. “Σύστημα πρόβλεψης της βραχυχρόνιας τάσης της τιμής των μετοχών με χρήση νευρο-ασαφών μεθόδων.” 2006. Web. 27 Feb 2021.

Vancouver:

Ατσαλάκης �. Σύστημα πρόβλεψης της βραχυχρόνιας τάσης της τιμής των μετοχών με χρήση νευρο-ασαφών μεθόδων. [Internet] [Thesis]. Technical University of Crete (TUC); Πολυτεχνείο Κρήτης; 2006. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/10442/hedi/14420.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ατσαλάκης �. Σύστημα πρόβλεψης της βραχυχρόνιας τάσης της τιμής των μετοχών με χρήση νευρο-ασαφών μεθόδων. [Thesis]. Technical University of Crete (TUC); Πολυτεχνείο Κρήτης; 2006. Available from: http://hdl.handle.net/10442/hedi/14420

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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