Sliding mode-based adaptive learning in dynamical-filter-weights neurons

Hebertt Sira-Ramírez, Eliezer Colina-Morles, Francklin Rivas-Echeverría

Research output: Contribution to journalArticlepeer-review


A sliding mode control strategy is proposed for the synthesis of an adaptive learning algorithm in a neuron whose weights are constituted by first-order dynamical filters with adjustable parameters, which in turn allows the representation of dynamical processes in terms of a set of such neurons. The approach is shown to exhibit robustness characteristics 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)678-685
Number of pages8
JournalInternational Journal of Control
Issue number8
StatePublished - May 20 2000
Externally publishedYes

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications


Dive into the research topics of 'Sliding mode-based adaptive learning in dynamical-filter-weights neurons'. Together they form a unique fingerprint.

Cite this