Temat: static neural networks - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Stability and dissipativity analysis for neutral type stochastic Markovian jump static neural networks with time delays
Autorzy:: Cao, Yang
Samidurai, R.
Sriraman, R.
Powiązania:: https://bibliotekanauki.pl/articles/91527.pdf
Data publikacji:: 2019
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: static neural networks
dissipativity analysis
Markovian jump
time-varying delays
Opis:: This paper studies the global asymptotic stability and dissipativity problem for a class of neutral type stochastic Markovian Jump Static Neural Networks (NTSMJSNNs) with time-varying delays. By constructing an appropriate Lyapunov-Krasovskii Functional (LKF) with some augmented delay-dependent terms and by using integral inequalities to bound the derivative of the integral terms, some new sufficient conditions have been obtained, which ensure that the global asymptotic stability in the mean square. The results obtained in this paper are expressed in terms of Strict Linear Matrix Inequalities (LMIs), whose feasible solutions can be verified by effective MATLAB LMI control toolbox. Finally, examples and simulations are given to show the validity and advantages of the proposed results.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2019, 9, 3; 189-204
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

Artykuł

Skocz do pozycji: 2.

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

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