Local stability conditions for discrete-time cascade locally recurrent neural networks

• Autorzy: Krzysztof Patan
• Języki publikacji: EN

Abstrakty

EN

The paper deals with a specific kind of discrete-time recurrent neural network designed with dynamic neuron models. Dynamics are reproduced within each single neuron, hence the network considered is a locally recurrent globally feedforward. A crucial problem with neural networks of the dynamic type is stability as well as stabilization in learning problems. The paper formulates local stability conditions for the analysed class of neural networks using Lyapunov's first method. Moreover, a stabilization problem is defined and solved as a constrained optimization task. In order to tackle this problem, a gradient projection method is adopted. The efficiency and usefulness of the proposed approach are justified by using a number of experiments.

Słowa kluczowe

EN

locally recurrent neural network; stability; stabilization; learning; constrained optimization

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2010
• Tom: 20
• Numer: 1
• Strony: 23-34

Daty

wydano

2010

otrzymano

2009-02-04

poprawiono

2009-10-14

Twórcy

autor

Krzysztof Patan

• Institute of Control and Computation Engineering, University of Zielona Góra, ul. Podgórna 50, 65-246 Zielona Góra, Poland

Bibliografia

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Identyfikatory

DOI

10.2478/v10006-010-0002-x