Sliding mode-based adaptive learning in dynamical adalines

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

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)937-942
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
StatePublished - 1997
Externally publishedYes
EventProceedings of the 1997 36th IEEE Conference on Decision and Control. Part 1 (of 5) - San Diego, CA, USA
Duration: Dec 10 1997Dec 12 1997

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization


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