Temat: neuro-fuzzy control - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Neuro-fuzzy control design of processes in chemical technologies
Autorzy:: Blahová, L.
Dvoran, J.
Kmeťová, J.
Powiązania:: https://bibliotekanauki.pl/articles/229832.pdf
Data publikacji:: 2012
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: neuro-fuzzy control
chemical reactor
neural predictive controller
ANFIS
laboratory process
Opis:: The paper presents design of neuro-fuzzy control and its application in chemical technologies. Our approach to neuro-fuzzy control is a combination of the neural predictive controller and the neuro-fuzzy controller (Adaptive Network-based Fuzzy Inference System - ANFIS). These controllers work in parallel. The output of ANFIS adjusts the output of the neural predictive controller to enhance the control performance. Such design of an intelligent control system is applied to control of the continuous stirred tank reactor and laboratory mixing process.
Źródło:: Archives of Control Sciences; 2012, 22, 2; 233-250
1230-2384
Pojawia się w:: Archives of Control Sciences
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Performance analysis of the sensorless adaptive sliding-mode neuro-fuzzy control of the induction motor drive with MRAS-type speed estimator
Autorzy:: Orłowska-Kowalska, T.
Dybkowski, M.
Powiązania:: https://bibliotekanauki.pl/articles/201110.pdf
Data publikacji:: 2012
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: electrical drive
induction motor
adaptive control
sliding mode control
neuro-fuzzy control
sensorless control
Opis:: This paper discusses a model reference adaptive sliding-mode control of the sensorless vector controlled induction motor drive in a wide speed range. The adaptive speed controller uses on-line trained fuzzy neural network, which enables very fast tracking of the changing speed reference signal. This adaptive sliding-mode neuro-fuzzy controller (ASNFC) is used as a speed controller in the direct rotor-field oriented control (DRFOC) of the induction motor (IM) drive structure. Connective weights of the controller are trained on-line according to the error between the actual speed of the drive and the reference model output signal. The rotor flux and speed of the vector controlled induction motor are estimated using the model reference adaptive system (MRAS) – type estimator. Presented simulation results are verified by experimental tests performed on the laboratory-rig with DSP controller.
Źródło:: Bulletin of the Polish Academy of Sciences. Technical Sciences; 2012, 60, 1; 61-70
0239-7528
Pojawia się w:: Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Emotional learning based intelligent speed and position control applied to neurofuzzy model of switched reluctance motor
Autorzy:: Rouhani, H.
Sadeghzadeh, A.
Lucas, C.
Araabi, B. N.
Powiązania:: https://bibliotekanauki.pl/articles/969753.pdf
Data publikacji:: 2007
Wydawca:: Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:: intelligent control
emotion based learning
neuro-fuzzy models
switched reluctance motor
Opis:: In this paper, rotor speed and position of a Switched Reluctance Motor (SRM) are controlled using an intelligent control algorithm. The controller is working based on a PID signal while its gain is permanently tuned by means of an Emotional Learning Algorithm to achieve a better control performance. Here, nonlinear characteristic of SRM is identified using an efficient training algorithm (LoLiMoT) for Locally Linear Neurofuzzy Model as an unspecified nonlinear plant model. Then, the Brain Emotional Learning Based Intelligent Controller (BELBIC) is applied to the obtained model. While the intelligent controller works based on a computational model of a limbic system in the mammalian brain, its contribution is to improve the performance of a classic controller like PID without much more control effort. The results demonstrate excellent improvements of control action in different working situations.
Źródło:: Control and Cybernetics; 2007, 36, 1; 75-95
0324-8569
Pojawia się w:: Control and Cybernetics
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Neuro-fuzzy control of a robotic manipulator
Autorzy:: Gierlak, P.
Muszyńska, M.
Żylski, W.
Powiązania:: https://bibliotekanauki.pl/articles/955199.pdf
Data publikacji:: 2014
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: manipulator
sztuczna inteligencja
sieci neuronowe
robot
robotic manipulator
force control
neuro-fuzzy system
Opis:: In this paper, to solve the problem of control of a robotic manipulator’s movement with holonomical constraints, an intelligent control system was used. This system is understood as a hybrid controller, being a combination of fuzzy logic and an artificial neural network. The purpose of the neuro-fuzzy system is the approximation of the nonlinearity of the robotic manipulator’s dynamic to generate a compensatory control. The control system is designed in such a way as to permit modification of its properties under different operating conditions of the two-link manipulator.
Źródło:: International Journal of Applied Mechanics and Engineering; 2014, 19, 3; 575-584
1734-4492
2353-9003
Pojawia się w:: International Journal of Applied Mechanics and Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Features of control processes in organizational-technical (technological) systems of continuous type
Autorzy:: Korobiichuk, Igor
Ladanyuk, Anatoliy
Boiko, Regina
Hrybkov, Serhii
Powiązania:: https://bibliotekanauki.pl/articles/2141889.pdf
Data publikacji:: 2020
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: sugar production
control object
technological monitoring
automated complexes
uncertainty of information
neuro-fuzzy networks
Opis:: Technological complexes of various industries are characterized by certain modes of operation (technological regulations), which correspond to the set of variables of different nature, which have a high-dynamics of change and determine the main technical and economic performance of the object. The aim of the research is to identify information software approaches to support decision-making in organizational-technical (technological) systems. Research results are obtained through grouping, generalization and comparison methods. The scientific significance of the results are to determine the objective need to use intelligent decision support subsystems to quickly manage complex organizational-technical systems based on both: clear and formalized data and knowledge and high-quality fuzzy estimates.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2020, 14, 4; 11-17
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 6.

Tytuł:: Robustifying analysis of the direct adaptive control of unknown multivariable nonlinear systems based on a new neuro-fuzzy method
Autorzy:: Theodoridis, D. C.
Boutalis, Y.S.
Christodoulou, M. A.
Powiązania:: https://bibliotekanauki.pl/articles/91598.pdf
Data publikacji:: 2011
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: nonlinear systems
control
neuro-fuzzy dynamical system
fuzzy systems
FS
fuzzy recurrent high order neural network
F-RHONN
adaptive regulator
parameter
“Hopping”
“Modified Hopping”
modeling errors
asymptotic regulation
Opis:: In this paper, we are dealing with the problem of directly regulating unknown multivariable affine in the control nonlinear systems and its robustness analysis. The method employs a new Neuro-Fuzzy Dynamical System definition, which uses the concept of Fuzzy Systems (FS) operating in conjunction with High Order Neural Networks. In this way the unknown plant is modeled by a fuzzy - recurrent high order neural network structure (F-RHONN), which is of the known structure considering the neglected nonlinearities. The development is combined with a sensitivity analysis of the closed loop in the presence of modeling imperfections and provides a comprehensive and rigorous analysis showing that our adaptive regulator can guarantee the convergence of states to zero or at least uniform ultimate boundedness of all signals in the closed loop when a not-necessarily-known modeling error is applied. The existence and boundedness of the control signal is always assured by employing a method of parameter “Hopping” and “Modified Hopping”, which appears in the weight updating laws. Simulations illustrate the potency of the method showing that by following the proposed procedure one can obtain asymptotic regulation despite the presence of modeling errors. Comparisons are also made to simple recurrent high order neural network (RHONN) controllers, showing that our approach is superior to the case of simple RHONN’s.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2011, 1, 1; 59-79
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

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