Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "neural networks control" wg kryterium: Temat

  Lista filtrów
Wyrównaj
Wyświetlanie 1-15 z 55
Tytuł:
Dedicated neural network design for friction compensation in robot drives
Projektowanie struktury sieci neuronowej dla celów eliminacji tarcia w napędach robotów
Autorzy:
Korendo, Z.
Uhl, T.
Powiązania:
https://bibliotekanauki.pl/articles/281390.pdf
Data publikacji:
2002
Wydawca:
Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
Tematy:
friction modelling
mechatronics
neural networks for control
Opis:
In the paper we demonstrate a neural network-based controller design and prototyping following the mechatronic approach. A unified treatment of all system components (mechanical, eletrical and computational) is made possible thanks to the integrated software-hardware platform. The neural network in the presented approach is used to privide a linearising feedback loop for friction compensation in a robot drive. The efficiency of the experimental friction identification is improved thanks to dedicated network architecture. The proposed solution is implemented in DSP hardware and the simulation results are verified through laboratory experiments.
W pracy przedstawiono oparty na sieciach neuronowych układ sterowania napędem robota. Przedstawiono proces projektowania i prototypowania oparty na podejściu mechatronicznym. Sieć neuronowa w proponowanym rozwiązaniu spełnia rolę lineryzującej pętli sprzężenia zwrotnego. Jej podstawowym zadaniem jest kompensacja wpływu tarcia w napędzie robota. Zaproponowano specjalizowaną architekturę sieci neuronowej dostosowaną do modelowania tarcia. Uczenie sieci odbywa się na podstawie danych eksperymentalnych. Zaproponowaną sieć neuronową zaimplementowano z zastosowaniem techniki szybkiego prototypowania z wykorzystaniem procesorów sygnałowych. Wyniki symulacji porównano z wynikami eksperymentu na rzeczywistym obiekcie. Przedstawione podejście, jak wykazały uzyskane rezultaty, daje dobre wyniki w zakresie linearyzacji układów sterowania robotami z uwzględnieniem tarcia.
Źródło:
Journal of Theoretical and Applied Mechanics; 2002, 40, 3; 595-610
1429-2955
Pojawia się w:
Journal of Theoretical and Applied Mechanics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A stability based neural networks controller design method
Autorzy:
Song, J.
Xu, X.
He, X.
Powiązania:
https://bibliotekanauki.pl/articles/206120.pdf
Data publikacji:
1998
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
sieć neuronowa
stabilność
sterowanie nieliniowe
neural networks control
nonlinear control
sliding mode
stability
Opis:
The use of neural networks in control systems can be seen as a natural step in the evolution of control methodology to meet new challenges. Many attempts have been made to apply the neural networks to deal with non-linearities and uncertainties of the control systems. Research in neural network applications to control can be classified according to the major methods depending on structures of the control system, such as NN-based NON-linear System Identification, NN-based Supervised Control, NN-based Direct Control, NN-based Indirect Control, NN-based Adaptive Control, NN-based Self-learning Control, NN-based Fuzzy Control, and NN Variable Structure Control. All these control methods cannot, however, effectively guarantee system stability, i.e. none of these neural network controls, except for NN-based Variable Structure Control, is based on system stability. This also limits the application and development of the neural networks in control theory. The paper shows the effort to solve this difficulty and give a way for the design method of the stability based neural networks controller using Lyapunov second stability theorem. This kind of controller can not only guarantee system stability, but also fully compensate for the influence of system uncertainties and non-linearities.Simulation results also show the effectiveness of the controller.
Źródło:
Control and Cybernetics; 1998, 27, 1; 119-133
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Inversion of fuzzy neural networks for the reduction of noise in the control loop for automotive applications
Autorzy:
Nentwig, M.
Mercorelli, P.
Powiązania:
https://bibliotekanauki.pl/articles/384669.pdf
Data publikacji:
2009
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
neural networks
fuzzy control
inversion of neural networks
automotive control
noise reduction
Opis:
A robust throttle valve control has been an attractive problem since throttle by wire systems were established in the mid-nineties. Control strategies often use a feed-forward controller which use an inverse model; however, mathematical model inversions imply a high order of differentiation of the state variables resulting in noise effects. In general, neural networks are a very effective and popular tool for modelling. The inversion of a neural network makes it possible to use these networks in control problem schemes. This paper presents a control strategy based upon an inversion of a feed-forward trained local linear model tree. The local linear model tree is realized through a fuzzy neural network. Simulated results from real data measurements are presented, and two control loops are explicitly compared.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2009, 3, 3; 83-89
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Smart control based on neural networks for multicellular converters
Autorzy:
Laidi, Kamel
Bouchhida, Ouahid
Nibouche, Mokhtar
Benmansour, Khelifa
Powiązania:
https://bibliotekanauki.pl/articles/1841217.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
multicellular converters
neural networks
smart control
Opis:
A smart control based on neural networks for multicellular converters has been developed and implemented. The approach is based on a behavioral description of the different converter operating modes. Each operating mode represents a well-defined configuration for which an operating zone satisfying given invariance conditions, depending on the capacitors’ voltages and the load current of the converter, is assigned. A control vector, whose components are the control signals to be applied to the converter switches is generated for each mode. Therefore, generating the control signals becomes a classification task of the different operating zones. For this purpose, a neural approach has been developed and implemented to control a 2-cell converter then extended to a 3-cell converter. The developed approach has been compared to super-twisting sliding mode algorithm. The obtained results demonstrate the approach effectiveness to provide an efficient and robust control of the load current and ensure the balancing of the capacitors voltages.
Źródło:
Archives of Electrical Engineering; 2021, 70, 3; 531-550
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Suboptimal Non-linear Predictive Control Based on MLP and RBF Neural Models with Measured Disturbance Compensation
Autorzy:
Ławryńczuk, M.
Powiązania:
https://bibliotekanauki.pl/articles/384285.pdf
Data publikacji:
2008
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
predictive control
neural networks
linearisation
quadratic programming
Opis:
This paper is concerned with a computationally efficient (suboptimal) non-linear Model Predictive Control (MPC) algorithm based on two types of neural models: Multilayer Perceptron (MLP) and Radial Basis Function (RBF) structures. The model takes into account not only controlled but also the uncontrolled input of the process, i.e. the measured disturbance. The algorithm is computationally efficient, because it results in a quadratic programming problem, which can be effectively solved on-line by means of a numerically reliable software subroutine. Moreover, the algorithm gives good closed-loop control performance, comparable to that obtained in the fully-fledged non-linear MPC technique, which hinges on non-linear, usually non-convex optimisation.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2008, 2, 2; 54-64
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neural Network-Based Narx Models in Non-Linear Adaptive Control
Autorzy:
Dzieliński, A.
Powiązania:
https://bibliotekanauki.pl/articles/907986.pdf
Data publikacji:
2002
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
automatyka
neural networks
adaptive control
nonlinear systems
Opis:
The applicability of approximate NARX models of non-linear dynamic systems is discussed. The models are obtained by a new version of Fourier analysis-based neural network also described in the paper. This constitutes a reformulation of a known method in a recursive manner, i.e. adapted to account for incoming data on-line. The method allows us to obtain an approximate model of the non-linear system. The estimation of the influence of the modelling error on the discrepancy between the model and real system outputs is given. Possible applications of this approach to the design of BIBO stable closed-loop control are proposed.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2002, 12, 2; 235-240
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Elman neural network for modeling and predictive control of delayed dynamic systems
Autorzy:
Wysocki, A.
Ławryńczuk, M.
Powiązania:
https://bibliotekanauki.pl/articles/229646.pdf
Data publikacji:
2016
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
dynamic models
process control
model predictive control
neural networks
Elman neural network
delayed systems
Opis:
The objective of this paper is to present a modified structure and a training algorithm of the recurrent Elman neural network which makes it possible to explicitly take into account the time-delay of the process and a Model Predictive Control (MPC) algorithm for such a network. In MPC the predicted output trajectory is repeatedly linearized on-line along the future input trajectory, which leads to a quadratic optimization problem, nonlinear optimization is not necessary. A strongly nonlinear benchmark process (a simulated neutralization reactor) is considered to show advantages of the modified Elman neural network and the discussed MPC algorithm. The modified neural model is more precise and has a lower number of parameters in comparison with the classical Elman structure. The discussed MPC algorithm with on-line linearization gives similar trajectories as MPC with nonlinear optimization repeated at each sampling instant.
Źródło:
Archives of Control Sciences; 2016, 26, 1; 117-142
1230-2384
Pojawia się w:
Archives of Control Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neurocontrolled car speed system
Autorzy:
Nakonechnyi, Markiyan
Ivakhiv, Orest
Świsulski, Dariusz
Powiązania:
https://bibliotekanauki.pl/articles/27314203.pdf
Data publikacji:
2022
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
neural controller
PID-algorithm of control
dynamic object
neural networks
electric car
speed control
Opis:
The features of the synthesis of neural controllers for the car speed control system are considered in this article. The task of synthesis is to determine the weight coefficients of neural networks that provide the implementation of proportional and proportional-integralderivative control laws. The synthesis of controllers is based on an approach that uses a reversed model of the standard. A model of the car speed control system with the use of permitting subsystems has been developed, with the help of the synthesized controller that is connected under certain specified conditions. With the iterative programming and mathematical modeling environment in MATLAB, and using the Simulink package, a structural scheme for controlling the speed of the car was constructed and simulated using synthesized neural controllers.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2022, 16, 3; 13--21
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Zastosowanie sztucznych sieci neuronowych w doborze funkcji napędowych żurawi na podatnym podłożu
Application of the neural network in control of a flexibly supported crane
Autorzy:
Urbaś, A.
Szczotka, M.
Powiązania:
https://bibliotekanauki.pl/articles/386340.pdf
Data publikacji:
2010
Wydawca:
Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
Tematy:
sieci neuronowe
żuraw
sterowanie
neural networks
crane
control
Opis:
W artykule przedstawiono model dynamiczny układu żurawia chwytakowego posadowionego podatnie. Model posłużył do wykonania optymalizacji dynamicznej celem doboru funkcji napędowych ruchu obrotu i wysięgu zapewniających ruch ładunku po zadanej trajektorii (okręgu) oraz stabilizację ładunku po zatrzymaniu, mimo podatności podparcia. Ze względu na długi czas obliczeń optymalizacyjnych, model jest mało przydatny do sterowania układem w czasie rzeczywistym. Zastosowano zatem metodologię sztucznych sieci neuronowych, które generują odpowiedź dla układu sterowania w czasie znacznie krótszym.
The paper presents the application of the neural network to real-time control of drive characteristics. The mathematical model of the crane, which is flexibly supported, is considered. The problem of finding an optimal drive function can be solved by mean of the classic optimisation methods. However, due to long calculation time, this approach can not be applied in the crane control tasks in practice. Utilising the advantage of neural networks (fast response, ability to generalisation), we are able to obtain the courses of drive functions in the real-time. A well trained network can be then used also for other inputs, than those used during the training task. Some example of simulations have been presented in the article.
Źródło:
Acta Mechanica et Automatica; 2010, 4, 1; 101-107
1898-4088
2300-5319
Pojawia się w:
Acta Mechanica et Automatica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Zastosowanie sieci neuronowej do sterowania pracą wciągarki
Application of neural network to define winch drive function
Autorzy:
Falat, P.
Powiązania:
https://bibliotekanauki.pl/articles/387180.pdf
Data publikacji:
2010
Wydawca:
Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
Tematy:
sterowanie
maszyna
sieci neuronowe
control
machine
neural networks
Opis:
Często spotykanym zadaniem stawianym przed żurawiami typu offshore jest utrzymanie ładunku na zadanej głębokości pomimo ruchów bazy żurawia wywołanych falowaniem morza. W niniejszym artykule przedstawiono zastosowa- nie sieci neuronowej do wyznaczania funkcji napędowej wciągarki zapewniającej stabilizację pionową ładunku. Analizy przeprowadzono dla żurawia typu A-rama. Opracowano jego model matematyczny, przy czym korzystając z wcześniejszych doświadczeń autora, przyjęto, że jedynym elementem podatnym w układzie jest lina. W poprzednich pracach autor proponował użycie metod optymalizacji do wyznaczenia poszukiwanej funkcji napędowej. Optymalizacja dynamiczna jest jednak na tyle czasochłonna obliczeniowo, że jej zastosowanie w czasie rzeczywistym nie jest możliwe. Stąd podjęto próbę wykorzystania do rozwiązania tego problemu sieci neuronowej. Optymalizację zastosowano natomiast do przygotowania odpowiedniego zbioru uczącego dla sieci neuronowej.
The paper presents the application of the neural network to controlling of the drive function of a sea crane winch. The function ensures the load stabilization on a proper depth. The base ship movements, caused by the sea waves, are taken into consideration. The author applied the neural network and the object oriented programming techniques which have been used to crate own software applications.
Źródło:
Acta Mechanica et Automatica; 2010, 4, 2; 43-48
1898-4088
2300-5319
Pojawia się w:
Acta Mechanica et Automatica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An adaptive control system of roadheader with intelligent modelling of mechanical features of mined rock
Autorzy:
Jasiulek, D.
Stankiewicz, K.
Świder, J.
Powiązania:
https://bibliotekanauki.pl/articles/242129.pdf
Data publikacji:
2011
Wydawca:
Instytut Techniczny Wojsk Lotniczych
Tematy:
roadheader
artificial neural networks
rocks properties
control system
Opis:
An idea of use of artificial intelligence technology for determination of selected parameters of roadheader operation, by a direct implementation of artificial neural network in control system of machine, was presented in the paper. The roadheaders operates in hard coal mines underground in extremely difficult environmental conditions. Technological process of driving of roadheader depends on many factors such as technical parameters of machine, mechanical and physical properties of rocks and operator's skills. It is difficult to develop a conventional system that could help in control operation of actuators of the machine, and increase the utilization of machine technical potential and improve rate of roadway development advance, due to mining-and-geological conditions (including mechanical and physical features of rocks), which are variable during mining. Proposed system for control of roadheader, as an adaptive system equipped with artificial neural network, will react to changes in operational space of machine. Improved machine performance will be possible due to use of artificial intelligence technology, which aids analysis of conditions of machine operation such as type of mined rock, size of excavation or web depth, makes possible inference process of introducing adequate changes of actuators control values.
Źródło:
Journal of KONES; 2011, 18, 2; 197-203
1231-4005
2354-0133
Pojawia się w:
Journal of KONES
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fuzzy and Neural Control of an Induction Motor
Autorzy:
Denai, M., A.
Attia, S. A.
Powiązania:
https://bibliotekanauki.pl/articles/908003.pdf
Data publikacji:
2002
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
automatyka
fuzzy control
neural networks
induction motor
vector control
speed observer
Opis:
This paper presents some design approaches to hybrid control systems combining conventional control techniques with fuzzy logic and neural networks. Such a mixed implementation leads to a more effective control design with improved system performance and robustness. While conventional control allows different design objectives such as steady state and transient characteristics of the closed loop system to be specified, fuzzy logic and neural networks are integrated to overcome the problems with uncertainties in the plant parameters and structure encountered in the classical model-based design. Induction motors are characterised by complex, highly non-linear and time-varying dynamics and inaccessibility of some states and outputs for measurements, and hence can be considered as a challenging engineering problem. The advent of vector control techniques has partially solved induction motor control problems, because they are sensitive to drive parameter variations and performance may deteriorate if conventional controllers are used. Fuzzy logic and neural network-based controllers are considered as potential candidates for such an application. Three control approaches are developed and applied to adjust the speed of the drive system. The first control design combines the variable structure theory with the fuzzy logic concept. In the second approach neural networks are used in an internal model control structure. Finally, a fuzzy state feedback controller is developed based on the pole placement technique. A simulation study of these methods is presented. The effectiveness of these controllers is demonstrated for different operating conditions of the drive system.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2002, 12, 2; 221-233
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Stator winding fault diagnosis of induction motor operating under the field-oriented control with convolutional neural networks
Autorzy:
Skowron, M.
Wolkiewicz, M.
Tarchała, G.
Powiązania:
https://bibliotekanauki.pl/articles/200241.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
diagnostics
stator faults
field-oriented control
convolutional neural networks
Opis:
In this paper deep neural networks are proposed to diagnose inter-turn short-circuits of induction motor stator windings operating under the Direct Field Oriented Control method. A convolutional neural network (CNN), trained with a Stochastic Gradient Descent with Momentum method is used. This kind of deep-trained neural network allows to significantly accelerate the diagnostic process compared to the traditional methods based on the Fast Fourier Transform as well as it does not require stationary operating conditions. To assess the effectiveness of the applied CNN-based detectors, the tests were carried out for variable load conditions and different values of the supply voltage frequency. Experimental results of the proposed induction motor fault detection system are presented and discussed.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2020, 68, 5; 1039-1048
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neural model of the vehicle control system in a racing game. Part 2, Research experiments
Autorzy:
Bolesta, Arkadiusz
Tchórzewski, Jerzy
Powiązania:
https://bibliotekanauki.pl/articles/2175161.pdf
Data publikacji:
2022
Wydawca:
Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
Tematy:
Godot Engine
MATLAB
Simulink environment
Neural control system
Perceptron Artificial Neural Networks
video games
Opis:
This article, which is a continuation of the article under the same main title and subtitle: part 1 Design and its implementation, includes the obtained results of research experiments with the use of a designed and implemented racing game. It uses a neural model of the vehicle motion control system on the racetrack in the form of a Perceptron Artificial Neural Network (ANN). In designing the movement of vehicles on the racetrack, the following were used, inter alia, Godot Engine and MATLAB and Simulink programming environment. The numerical data (14 input quantities and two output quantities) for ANN training were prepared with the use of semi-automatic measurement of the race track control points. This article shows, among others, the results of 10 selected research experiments, testing and simulation, confirming the correct functioning of both the computer game and the model of the neural control system. As a result of simulation tests, it turned out that the longest lap of the track in the conducted experiments lasted 4 minutes and 55 seconds, and the shortest - 10.47 seconds. In five minutes, the highest number of laps was 34, while the lowest numbers of laps were 1 and 5. In the course of the experiments it was noticed that under the same conditions the ANN learning outcomes are sometimes different.
Źródło:
Studia Informatica : systems and information technology; 2022, 1(26); 45--60
1731-2264
Pojawia się w:
Studia Informatica : systems and information technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Improving the Generalization Ability of Neuro-Fuzzy Systems by e-Insensitive Learning
Autorzy:
Łęski, J.
Powiązania:
https://bibliotekanauki.pl/articles/908037.pdf
Data publikacji:
2002
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
informatyka
fuzzy systems
neural networks
tolerant learning
generalization control
robust methods
Opis:
A new learning method tolerant of imprecision is introduced and used in neuro-fuzzy modelling. The proposed method makes it possible to dispose of an intrinsic inconsistency of neuro-fuzzy modelling, where zero-tolerance learning is used to obtain a fuzzy model tolerant of imprecision. This new method can be called e-insensitive learning, where, in order to fit the fuzzy model to real data, the e-insensitive loss function is used. e-insensitive learning leads to a model with minimal Vapnik-Chervonenkis dimension, which results in an improved generalization ability of this system. Another advantage of the proposed method is its robustness against outliers. This paper introduces two approaches to solving e-insensitive learning problem. The first approach leads to a quadratic programming problem with bound constraints and one linear equality constraint. The second approach leads to a problem of solving a system of linear inequalities. Two computationally efficient numerical methods for e-insensitive learning are proposed. Finally, examples are given to demonstrate the validity of the introduced methods.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2002, 12, 3; 437-447
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 55

Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies