Dynamic Neural Networks for Process Modelling in Fault Detection and Isolation Systems

• Autorzy: Korbicz J. , Patan K. , Obuchowicz A.
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

fault detection; dynamic neural networks; non-linear modelling; learning algorithms; FL-classifier; two-tank system

PL

wykrywanie błędu; dynamiczne sieci neuronowe; modelowanie nieliniowe; algorytm inteligentny

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 1999
• Tom: Vol. 9, no 3
• Strony: 519--546
• Opis fizyczny: Bibliogr. 47 poz., rys., tab., wykr.

Twórcy

autor

Korbicz J.

autor

Patan K.

autor

Obuchowicz A.

• Department of Robotics and Software Engineering Technical University of Zielona Gora ul. Podgorna 50, 65-246 Zielona Gora,Poland,