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Wyszukujesz frazę "sieć neuronowa dynamiczna" wg kryterium: Temat


Wyświetlanie 1-4 z 4
Tytuł:
Towards robustness in neural network based fault diagnosis
Autorzy:
Patan, K.
Witczak, M.
Korbicz, J.
Powiązania:
https://bibliotekanauki.pl/articles/929913.pdf
Data publikacji:
2008
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
diagnostyka uszkodzeń
odporność
sieć neuronowa dynamiczna
fault diagnosis
robustness
dynamic neural networks
GMDH neural network
Opis:
Challenging design problems arise regularly in modern fault diagnosis systems. Unfortunately, classical analytical techniques often cannot provide acceptable solutions to such difficult tasks. This explains why soft computing techniques such as neural networks become more and more popular in industrial applications of fault diagnosis. Taking into account the two crucial aspects, i.e., the nonlinear behaviour of the system being diagnosed as well as the robustness of a fault diagnosis scheme with respect to modelling uncertainty, two different neural network based schemes are described and carefully discussed. The final part of the paper presents an illustrative example regarding the modelling and fault diagnosis of a DC motor, which shows the performance of the proposed strategy.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2008, 18, 4; 443-454
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Development of Dynamic Neural Networks With Application to Observer-Based Fault Detection and Izolation
Autorzy:
Marcu, T.
Mirea, L.
Frank, P. M.
Powiązania:
https://bibliotekanauki.pl/articles/908286.pdf
Data publikacji:
1999
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
wykrywanie błędu
sieć neuronowa dynamiczna
identyfikacja systemu
fault diagnosis
dynamic neural networks
system identification
static neural classifiers
three-tank system
Opis:
The paper suggests a neural-network approach to the design of robust fault diagnosis systems. The main emphasis is placed upon the development of neural observer schemes. They are built based on dynamic neural networks, i.e. dynamic multi-layer perceptrons with mixed structure. The goal is to achieve an adequate approximation of process outputs for known classes of the process behaviour. The obtained symptoms are then classified by means of static artificial nets. Appropriate decision mechanisms are designed for each type of observer schemes. An application to a laboratory process is included. It refers to component and instrument fault detection and isolation in a three-tank system.
Źródło:
International Journal of Applied Mathematics and Computer Science; 1999, 9, 3; 547-570
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An unscented Kalman filter in designing dynamic GMDH neural networks for robust fault detection
Autorzy:
Mrugalski, M.
Powiązania:
https://bibliotekanauki.pl/articles/331364.pdf
Data publikacji:
2013
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
robust fault detection
nonlinear system identification
dynamic GMDH neural network
unscented Kalman filter
detekcja uszkodzeń
identyfikacja nieliniowa
sieć neuronowa dynamiczna
filtr Kalmana bezśladowy
Opis:
This paper presents an identification method of dynamic systems based on a group method of data handling approach. In particular, a new structure of the dynamic multi-input multi-output neuron in a state-space representation is proposed. Moreover, a new training algorithm of the neural network based on the unscented Kalman filter is presented. The final part of the work contains an illustrative example regarding the application of the proposed approach to robust fault detection of a tunnel furnace.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2013, 23, 1; 157-169
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Dynamic Neural Networks for Process Modelling in Fault Detection and Isolation Systems
Autorzy:
Korbicz, J.
Patan, K.
Obuchowicz, A.
Powiązania:
https://bibliotekanauki.pl/articles/908291.pdf
Data publikacji:
1999
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
wykrywanie błędu
sieć neuronowa dynamiczna
modelowanie nieliniowe
algorytm inteligentny
fault detection
dynamic neural networks
non-linear modelling
learning algorithms
FL-classifier
two-tank system
Opis:
A fault diagnosis scheme for unknown nonlinear dynamic systems with modules of residual generation and residual evaluation is considered. Main emphasis is placed upon designing a bank of neural networks with dynamic neurons that model a system diagnosed at normal and faulty operating points.To improve the quality of neural modelling, two optimization problems are included in the construction of such dynamic networks: searching for an optimal network architecture and the network training algorithm. To find a good solution, the effective well-known cascade-correlation algorithm is adapted here. The residuals generated by a bank of neural models are then evaluated by means of pattern classification. To illustrate the effectiveness of our approach, two applications are presented: a neural model of Narendra's system and a fault detection and identification system for the two-tank process.
Źródło:
International Journal of Applied Mathematics and Computer Science; 1999, 9, 3; 519-546
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-4 z 4

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