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 "adaptive neural network" wg kryterium: Temat

  Lista filtrów
Wyrównaj
Wyświetlanie 1-15 z 20
Tytuł:
Exploration and mining learning robot of autonomous marine resources based on adaptive neural network controller
Autorzy:
Pan, L.
Powiązania:
https://bibliotekanauki.pl/articles/260396.pdf
Data publikacji:
2018
Wydawca:
Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:
adaptive neural network
marine resources
learning robot
Opis:
To study the autonomous learning model of the learning robot for marine resource exploration, an adaptive neural network controller was applied. The motion characteristics of autonomous learning robots were identified. The mathematical model of the multilayer forward neural network and its improved learning algorithm were studied. The improved Elman regression neural network and the composite input dynamic regression neural network were further discussed. At the same time, the diagonal neural network was analysed from the structure and learning algorithms. The results showed that for the complex environment of the ocean, the structure of the composite input dynamic regression network was simple, and the convergence was fast. In summary, the identification method of underwater robot system based on neural network is effective.
Źródło:
Polish Maritime Research; 2018, S 3; 78-83
1233-2585
Pojawia się w:
Polish Maritime Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Event-triggered adaptive neural network trajectory tracking control for underactuated ships under uncertain disturbance
Autorzy:
Su, Wenxue
Zhang, Qiang
Liu, Yufeng
Powiązania:
https://bibliotekanauki.pl/articles/34611659.pdf
Data publikacji:
2023
Wydawca:
Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:
event-triggered control
underactuated marine surface vessels
adaptive neural network
trajectory tracking
finite-time
Opis:
An adaptive neural network (NN) event-triggered trajectory tracking control scheme based on finite time convergence is proposed to address the problem of trajectory tracking control of underdriven surface ships. In this scheme, both NNs and minimum learning parameters (MLPS) are applied. The internal and external uncertainties are approximated by NNs. To reduce the computational complexity, MLPs are used in the proposed controller. An event-triggered technique is then incorporated into the control design to synthesise an adaptive NN-based event-triggered controller with finite-time convergence. Lyapunov theory is applied to prove that all signals are bounded in the tracking system of underactuated vessels, and to show that Zeno behavior can be avoided. The validity of this control scheme is determined based on simulation results, and comparisons with some alternative schemes are presented.
Źródło:
Polish Maritime Research; 2023, 3; 119-131
1233-2585
Pojawia się w:
Polish Maritime Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Comparison of the adaptive and neural network control for LWR 4+ manipulators: simulation study
Autorzy:
Woliński, Łukasz
Powiązania:
https://bibliotekanauki.pl/articles/140152.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
adaptive control
neural network control
redundant manipulator
Opis:
This paper deals with two control algorithms which utilize learning of their models’ parameters. An adaptive and artificial neural network control techniques are described and compared. Both control algorithms are implemented in MATLAB and Simulink environment, and they are used in the simulation of a postion control of the LWR 4+ manipulator subjected to unknown disturbances. The results, showing the better performance of the artificial neural network controller, are shown. Advantages and disadvantages of both controllers are discussed. The usefulness of the learning algorithms for the control of LWR 4+ robots is discussed. Preliminary experiments dealing with dynamic properties of the two LWR 4+ robots are reported.
Źródło:
Archive of Mechanical Engineering; 2020, LXVII, 1; 111-121
0004-0738
Pojawia się w:
Archive of Mechanical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Predictive neural network in multipurpose self-tuning controller
Autorzy:
Bondar, Oleksiy
Powiązania:
https://bibliotekanauki.pl/articles/386771.pdf
Data publikacji:
2020
Wydawca:
Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
Tematy:
artificial neural network
adaptive regulator
backpropagation algorithm
system modelling
Opis:
A very important problem in designing of controlling systems is to choose the right type of architecture of controller. And it is always a compromise between accuracy, difficulty in setting up, technical complexity and cost, expandability, flexibility and so on. In this paper, multipurpose adaptive controller with implementation of artificial neural network is offered as an answer to a wide range of tasks related to regulation. The effectiveness of the approach is demonstrated by the example of an adaptive thermostat. It also compares its capabilities with those of classic PID controller. The core of this approach is the use of an artificial neural network capable of predicting the behaviour of controlled object within its known range of parameters. Since such a network, being trained, is a model of a regulated system with arbitrary precision, it can be analysed to make optimal management decisions at the moment or in a number of steps. Network learning algorithm is backpropagation and its modified version is used to analyse an already trained network in order to find the optimal solution for the regulator. Software implementation, such as graphical user interface, routines related to neural network and many other, is done using Java programming language and Processing open-source integrated development environment.
Źródło:
Acta Mechanica et Automatica; 2020, 14, 2; 114-120
1898-4088
2300-5319
Pojawia się w:
Acta Mechanica et Automatica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Nonlinear Structural Acoustic Control with Shunt Circuit Governed by a Soft-Computing Algorithm
Autorzy:
Kurczyk, S.
Pawełczyk, M.
Powiązania:
https://bibliotekanauki.pl/articles/177699.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
Active Noise Control
adaptive control
neural network
vibrating plate
Opis:
Noise control has gained a lot of attention recently. However, presence of nonlinearities in Signac paths for some applications can cause significant difficulties in the operation of control algorithms. In particular, this problem is common in structural noise control, which uses a piezoelectric shunt circuit. Not only vibrating structures may exhibit nonlinear characteristics, but also piezoelectric actuators. In this paper, active device casing is addressed. The objective is to minimize the noise coming out of the casing, by controlling vibration of its walls. The shunt technology is applied. The proposed control algorithm is based on algorithms from a group of soft computing. It is verified by means of simulations using data acquired from a real object.
Źródło:
Archives of Acoustics; 2018, 43, 3; 397-402
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Adaptive control of autonomous underwater vehicle based on fuzzy neural network
Autorzy:
Qin, Z.
Gu, J.
Powiązania:
https://bibliotekanauki.pl/articles/384507.pdf
Data publikacji:
2010
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
autonomous underwater vehicle
fuzzy neural network
adaptive control
stability
Opis:
This paper presents an adaptive control method based on fuzzy neural network for Autonomous Underwater Vehicle (AUV). The Fuzzy Neural Network (FNN) could build the inverse model of AUV through on-line learning algorithm, which is free of fuzzy neural network structure knowledge and prior fuzzy inference rules. The adaptive controller for AUV based on FNN is proposed, and then the stability of the resulting AUV closed-loop control system is analyzed by Lyaponov stability theory. The validity of the proposed control method has been verified through computer simulation experiments.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2010, 4, 1; 104-111
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fuzzy logic and neural network approach to the indirect adaptive pole placement crane control system
Autorzy:
Smoczek, J.
Szpytko, J.
Powiązania:
https://bibliotekanauki.pl/articles/246396.pdf
Data publikacji:
2010
Wydawca:
Instytut Techniczny Wojsk Lotniczych
Tematy:
overhead crane
indirect adaptive
pole placement
fuzzy logic
neural network
Opis:
The problem under consideration in the paper of automation transportation operation realized by material handling devices is focused on time and accuracy of an overhead travelling crane's shifting process. The presented anti-sway crane control system was solved in the paper using combination of an indirect adaptive pole placement (IAPP) control method, fuzzy logic and artificial neural network. The presented approach to crane control is based on assuming structure of crane dynamic linear model with varying parameters, and linear closed-loop discrete control system consisting of proportional-derivative controllers with gains adjusted to changes of model's parameters using pole placement method (PPM). The parameters of crane dynamic model are estimated on-line using recursive least squares (RLS) algorithm. The estimation process is speeded up by neuro-fuzzy estimator, created using Takagi-Sugeno-Kang (TSK) fuzzy inference system, which determines the initial parameters of crane model based on scheduling variables, rope length and mass of a load changing in stochastic way. The neuro-fuzzy estimator is created in off-line process of neural network learning using least mean squares (LMS) method, based on a set of parametric output error models of crane dynamic identified for fixed values of rope length and mass of a load. The TSK estimator is next on-line improved by RLS algorithm.
Źródło:
Journal of KONES; 2010, 17, 2; 435-444
1231-4005
2354-0133
Pojawia się w:
Journal of KONES
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Recurrent neural identification and control of a continuous bioprocess via first and second order learning
Autorzy:
Baruch, I.
Mariaca-Gaspar, C. R.
Powiązania:
https://bibliotekanauki.pl/articles/385133.pdf
Data publikacji:
2010
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
backpropagation learning
direct adaptive neural control
indirect adaptive sliding mode control
Kalman filter recurrent neural network identifier
Levenberg-Marquardt learning
Opis:
This paper applies a new Kalman Filter Recurrent Neural Network (KFRNN) topology and a recursive Levenberg-Mar quardt (L-M) learning algorithm capable to estimate para meters and states of highly nonlinear unknown plant in noisy environment. The proposed KFRNN identifier, learned by the Backpropagation and L-M learning algorithm, was incorporated in a direct and indirect adaptive neural con trol schemes. The proposed control schemes were applied for real-time recurrent neural identification and control of a continuous stirred tank bioreactor model, where fast convergence, noise filtering and low mean squared error of reference tracking were achieved.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2010, 4, 4; 37-52
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fault diagnosis model of rolling bearing based on parameter adaptive VMD algorithm and Sparrow Search Algorithm-Based PNN
Autorzy:
Li, Junxing
Liu, Zhiwei
Qiu, Ming
Niu, Kaicen
Powiązania:
https://bibliotekanauki.pl/articles/24200836.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
rolling bearing
failure diagnosis
adaptive variational mode decomposition
sparrow probabilistic neural network
Opis:
Fault diagnosis of rolling bearings is essential to ensure the proper functioning of the entire machinery and equipment. Variational mode decomposition (VMD) and neural networks have gained widespread attention in the field of bearing fault diagnosis due to their powerful feature extraction and feature learning capacity. However, past methods usually utilize experiential knowledge to determine the key parameters in the VMD and neural networks, such as the penalty factor, the smooth factor, and so on, so that generates a poor diagnostic result. To address this problem, an Adaptive Variational Mode Decomposition (AVMD) is proposed to obtain better features to construct the fault feature matrix and Sparrow probabilistic neural network (SPNN) is constructed for rolling bearing fault diagnosis. Firstly, the unknown parameters of VMD are estimated by using the genetic algorithm (GA), then the suitable features such as kurtosis and singular value entropy are extracted by automatically adjusting the parameters of VMD. Furthermore, a probabilistic neural network (PNN) is used for bearing fault diagnosis. Meanwhile, embedding the sparrow search algorithm (SSA) into PNN to obtain the optimal smoothing factor. Finally, the proposed method is tested and evaluated on a public bearing dataset and bearing tests. The results demonstrate that the proposed method can extract suitable features and achieve high diagnostic accuracy.
Źródło:
Eksploatacja i Niezawodność; 2023, 25, 2; art. no. 163547
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Based on neural network adaptive linear quadratic regulator for inverter with voltage matching circuit
Autorzy:
Niewiara, Ł.
Tarczewski, T.
Grzesiak, L.M.
Powiązania:
https://bibliotekanauki.pl/articles/97686.pdf
Data publikacji:
2014
Wydawca:
Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
Tematy:
adaptive linear-quadratic regulator
voltage matching circuit
artificial neural network
current ripple
Opis:
This paper describes a discrete adaptive linear quadratic regulator used to load current control in terms of variable DC voltage of inverter. Controller was designed by using linear quadratic optimization method. Adaptive LQR was used because of non-stationarity of the control system caused by Voltage Matching Circuit - VMC. Gain values of the adaptive controller were approximated by using an artificial neural network. The VMC was realized as an additional buck converter integrated with the main inverter. As the load of the 2-level inverter a 3-phase symmetric RL circuit was used. Simulation tests show the behavior of the load current regulation during DC bus voltage level step changes. The dependence between current RMS value and inverter DC bus voltage level was also shown. There were also made a comparison of the traditional 2-level inverter structure with the modified structure uses VMC. Simulation test was made by using Matlab Simulink and PLECS software.
Źródło:
Computer Applications in Electrical Engineering; 2014, 12; 434-443
1508-4248
Pojawia się w:
Computer Applications in Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neural network based adaptive state feedback controller for inverter with voltage matching circuit
Autorzy:
Niewiara, Ł.
Tarczewski, T.
Grzesiak, L. M.
Powiązania:
https://bibliotekanauki.pl/articles/376361.pdf
Data publikacji:
2014
Wydawca:
Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
Tematy:
adaptive linear-quadratic controller
voltage matching circuit
artificial neural network
current ripple
Opis:
This paper describes a discrete adaptive state feedback controller used to load current control in terms of variable DC voltage of inverter. Controller was designed by using linear quadratic optimization method. Adaptive LQR was used because of non-stationarity of the control system caused by Voltage Matching Circuit - VMC. Gain values of the adaptive controller were approximated by using an artificial neural network. The VMC was realized as an additional buck converter integrated with the main inverter. As the load of the 2-level inverter a 3-phase symmetric RL circuit was used. Simulation tests show the behavior of the load current regulation during DC bus voltage level step changes. The dependence between current RMS value and inverter DC bus voltage level was also shown. Simulation test was made by using Matlab Simulink and PLECS software.
Źródło:
Poznan University of Technology Academic Journals. Electrical Engineering; 2014, 80; 231-238
1897-0737
Pojawia się w:
Poznan University of Technology Academic Journals. Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Pulverized coal combustion advanced control techniques
Zaawansowane metody sterowania procesem spalania pyłu węglowego
Autorzy:
Gromaszek, Konrad
Powiązania:
https://bibliotekanauki.pl/articles/408257.pdf
Data publikacji:
2019
Wydawca:
Politechnika Lubelska. Wydawnictwo Politechniki Lubelskiej
Tematy:
combustion control
adaptive algorithm
artificial neural network
sterowanie procesem
algorytm adaptacyjny
sztuczna sieć neuronowa
Opis:
The paper describes the selected methods of adaptive control of the pulverized coal combustion process overview with various types of prognostic models. It was proposed to use a class of control methods that are relatively well established in industrial practice. The presented approach distinguishes the use of an additional source of information in the form of signals from an optical diagnostic system and models based on selected deep structures of recurrent networks. The research aim is to increase the efficiency of the combustion process in the power boiler, taking into account the EU emission standards, leading in consequence to sustainable energy and sustainable environmental engineering.
W artykule opisano wybrane metody adaptacyjnego sterowania przeglądem procesu spalania pyłu węglowego z wykorzystaniem określonych modeli prognostycznych. Zaproponowano użycie metod, które są stosunkowo dobrze znane w praktyce przemysłowej. Przedstawione podejście wyróżnia wykorzystanie dodatkowego źródła informacji w postaci sygnałów z optycznego systemu diagnostycznego i modeli opartych na strukturach sieci głębokich. Badania mają na celu zwiększenia efektywności procesu spalania w kotle energetycznym, z uwzględnieniem norm emisji UE, prowadząc w konsekwencji do zrównoważonej energii i zrównoważonej inżynierii środowiska.
Źródło:
Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska; 2019, 9, 2; 41-45
2083-0157
2391-6761
Pojawia się w:
Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Forecasting return products in an integrated forward/reverse supply chain utilizing an ANFIS
Autorzy:
Kumar, D. T.
Soleimani, H.
Kannan, G.
Powiązania:
https://bibliotekanauki.pl/articles/329809.pdf
Data publikacji:
2014
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
artificial neural network
adaptive network based fuzzy
inference system
closed loop supply chain
forecasting methods
fuzzy neural network
sztuczna sieć neuronowa
system wnioskowania
metoda prognozowania
sieć neuronowa rozmyta
Opis:
Interests in Closed-Loop Supply Chain (CLSC) issues are growing day by day within the academia, companies, and customers. Many papers discuss profitability or cost reduction impacts of remanufacturing, but a very important point is almost missing. Indeed, there is no guarantee about the amounts of return products even if we know a lot about demands of first products. This uncertainty is due to reasons such as companies’ capabilities in collecting End-of-Life (EOL) products, customers’ interests in returning (and current incentives), and other independent collectors. The aim of this paper is to deal with the important gap of the uncertainties of return products. Therefore, we discuss the forecasting method of return products which have their own open-loop supply chain. We develop an integrated two-phase methodology to cope with the closed-loop supply chain design and planning problem. In the first phase, an Adaptive Network Based Fuzzy Inference System (ANFIS) is presented to handle the uncertainties of the amounts of return product and to determine the forecasted return rates. In the second phase, and based on the results of the first one, the proposed multi-echelon, multi-product, multi-period, closed-loop supply chain network is optimized. The second-phase optimization is undertaken based on using general exact solvers in order to achieve the global optimum. Finally, the performance of the proposed forecasting method is evaluated in 25 periods using a numerical example, which contains a pattern in the returning of products. The results reveal acceptable performance of the proposed two-phase optimization method. Based on them, such forecasting approaches can be applied to real-case CLSC problems in order to achieve more reliable design and planning of the network.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2014, 24, 3; 669-682
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Multi-Layer Perceptron Neural Network Utilizing Adaptive Best-Mass Gravitational Search Algorithm to Classify Sonar Dataset
Autorzy:
Mosavi, Mohammad Reza
Khishe, Mohammad
Naseri, Mohammad Jafar
Parvizi, Gholam Reza
Ayat, Mehdi
Powiązania:
https://bibliotekanauki.pl/articles/176971.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
MLP NN
Multi-Layer Perceptron Neural Network
ABGSA
Adaptive Best Mass Gravitational Search Algorithm
sonar
classification
Opis:
In this paper, a new Multi-Layer Perceptron Neural Network (MLP NN) classifier is proposed for classifying sonar targets and non-targets from the acoustic backscattered signals. Besides the capabilities of MLP NNs, it uses Back Propagation (BP) and Gradient Descent (GD) for training; therefore, MLP NNs face with not only impertinent classification accuracy but also getting stuck in local minima as well as lowconvergence speed. To lift defections, this study uses Adaptive Best Mass Gravitational Search Algorithm (ABGSA) to train MLP NN. This algorithm develops marginal disadvantage of the GSA using the bestcollected masses within iterations and expediting exploitation phase. To test the proposed classifier, this algorithm along with the GSA, GD, GA, PSO and compound method (PSOGSA) via three datasets in various dimensions will be assessed. Assessed metrics include convergence speed, fail probability in local minimum and classification accuracy. Finally, as a practical application assumed network classifies sonar dataset. This dataset consists of the backscattered echoes from six different objects: four targets and two non-targets. Results indicate that the new classifier proposes better output in terms of aforementioned criteria than whole proposed benchmarks.
Źródło:
Archives of Acoustics; 2019, 44, 1; 137-151
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Wpływ czasu odpowiedzi neurosterownika na jakość regulacji
Response time of neurodriver VS quality of regulation
Autorzy:
Lis, M.
Powiązania:
https://bibliotekanauki.pl/articles/408044.pdf
Data publikacji:
2012
Wydawca:
Politechnika Lubelska. Wydawnictwo Politechniki Lubelskiej
Tematy:
sieć neuronowa
adaptive interaction
jakość regulacji
czas odpowiedzi
neurosterownik
neural network
quality of regulation
response time
neurodriver
Opis:
W publikacji umieszczono wyniki dotyczące badania jakości sterowania siecią neuronową pracującą z różnym czasem wykonywania obliczeń. Celem jest wyznaczenie (dla założonego obiektu) granicy częstotliwości, z jaką musi pracować sieć, aby sterowanie spełniało założone wskaźniki jakości. Do uczenia sieci neuronowej wykorzystano algorytm adaptacyjnej interakcji.
In the article reader can find the results of testing the quality of neural network control with varying calculations time. The aim is to determine (for the assumed object) the limit frequency with which it must work to meet established quality indicators. Neural network learning algorithm uses the Adaptive Interaction method.
Źródło:
Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska; 2012, 3; 15-18
2083-0157
2391-6761
Pojawia się w:
Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 20

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