Sliding mode-based adaptive learning in dynamical adalines

Hebertt Sira-Ramirez; Eliezer Colina-Morles; Francklin Rivas-Echeverria

Sliding mode-based adaptive learning in dynamical adalines

Hebertt Sira-Ramirez^*
, Eliezer Colina-Morles
, Francklin Rivas-Echeverria

^*Autor correspondiente de este trabajo

Producción científica: Revista › Artículo de la conferencia › revisión exhaustiva

2 Citas (Scopus)

Resumen

A sliding mode control strategy is proposed for the synthesis of adaptive learning algorithms in perceptron-based feedforward neural networks whose weights are constituted by first order, time-varying, dynamical systems with adjustable parameters. The approach is shown to exhibit remarkable robustness and fast convergence properties. A simulation example, dealing with an analog signal tracking task, is provided which illustrates the feasibility of the approach.

Idioma original	Inglés
Páginas (desde-hasta)	937-942
Número de páginas	6
Publicación	Proceedings of the IEEE Conference on Decision and Control
Volumen	2
Estado	Publicada - 1997
Publicado de forma externa	Sí
Evento	Proceedings of the 1997 36th IEEE Conference on Decision and Control. Part 1 (of 5) - San Diego, CA, USA Duración: 10 dic. 1997 → 12 dic. 1997

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abstract = "A sliding mode control strategy is proposed for the synthesis of adaptive learning algorithms in perceptron-based feedforward neural networks whose weights are constituted by first order, time-varying, dynamical systems with adjustable parameters. The approach is shown to exhibit remarkable robustness and fast convergence properties. A simulation example, dealing with an analog signal tracking task, is provided which illustrates the feasibility of the approach.",

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AU - Sira-Ramirez, Hebertt

AU - Colina-Morles, Eliezer

AU - Rivas-Echeverria, Francklin

PY - 1997

Y1 - 1997

N2 - A sliding mode control strategy is proposed for the synthesis of adaptive learning algorithms in perceptron-based feedforward neural networks whose weights are constituted by first order, time-varying, dynamical systems with adjustable parameters. The approach is shown to exhibit remarkable robustness and fast convergence properties. A simulation example, dealing with an analog signal tracking task, is provided which illustrates the feasibility of the approach.

AB - A sliding mode control strategy is proposed for the synthesis of adaptive learning algorithms in perceptron-based feedforward neural networks whose weights are constituted by first order, time-varying, dynamical systems with adjustable parameters. The approach is shown to exhibit remarkable robustness and fast convergence properties. A simulation example, dealing with an analog signal tracking task, is provided which illustrates the feasibility of the approach.

UR - https://www.scopus.com/pages/publications/0031377140

M3 - Artículo de la conferencia

AN - SCOPUS:0031377140

SN - 0191-2216

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EP - 942

JO - Proceedings of the IEEE Conference on Decision and Control

JF - Proceedings of the IEEE Conference on Decision and Control

T2 - Proceedings of the 1997 36th IEEE Conference on Decision and Control. Part 1 (of 5)

Y2 - 10 December 1997 through 12 December 1997

ER -