Wyniki wyszukiwania - Biblioteka Nauki

Ten serwis zostanie wyłączony 2025-02-11.

Nowa wersja platformy, zawierająca wyłącznie zasoby pełnotekstowe, jest już dostępna.
Przejdź na https://bibliotekanauki.pl

Ograniczanie wyników

2 1999

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: sterowanie wewnętrzne

Sortuj według:

Ogranicz wyniki do:

Neural network based adaptive internal model control for nonlinear plants

100%

Ma Z. , Bajić V. B. , Ho D. W. C.

tom Vol. 7, nr 1

63-78

A novel non-parametric adaptive control method for nonlinear plants is proposed. It combines neural network (NN) based identification and internal model control (IMC) strategy. The NN is used to determine on-line an approximation of the unknown nonlinear process model. The NN parameters are updated according to the error between the plant output and the NN output. The NN can track the system output very well, so that an adaptive IMC can be implemented successfully. The design does not require computation of the inverse of the internal model of the process. Instead, it uses only system input-output data and NN output. The effectiveness of the proposed method is illustrated by a simulation experiment.

Neural Network Evaluation of Model-Based Residuals in Fault Detection of Time Delay Systems

100%

Zitek P. , Mankova R. , Hlava J.

tom Vol. 9, no 3

599-617

Model-based fault detection becomes rather questionable if a supervised plant belongs to the class of systems with distributed parameters and significant delays. Two methods of fault detection have been developed for this class of plants, namely a method of functional (anisochronic) state observer and a modified internal model control scheme adopted for that purpose. Both these model schemes are employed to generate residuals, i.e. differences suitable to watch whether a malfunction of the control operation has occurred. Continuous evaluation of residuals is provided by means of a dynamic application of artificial neural networks (ANNs). This evaluation is carried out on the basis of prediction of time series evolution, where the accordance obtained between the prediction and measured outputs is used as a classification criterion. Implementation of both the methods is demonstrated on a laboratory-scale heat transfer set-up, making use of the Real-Time Matlab software.