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Event-triggered H∞ filtering for delayed neural networks via sampled-data.

Emel Arslan1, R Vadivel2, M Syed Ali2

  • 1Istanbul University, Department of Computer Engineering, 34320 Avcilar, Istanbul, Turkey.

Neural Networks : the Official Journal of the International Neural Network Society
|May 2, 2017
PubMed
Summary

This study introduces an event-triggered H∞ filtering method for neural networks with delays, reducing data transmission. The approach ensures system stability and filter performance using advanced mathematical techniques.

Keywords:
filteringEvent-triggered schemeLyapunov methodNeural networksSampled dataTime-varying delay

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Area of Science:

  • Control Systems Engineering
  • Networked Systems
  • Artificial Intelligence

Background:

  • Neural networks with time delays are crucial in complex systems but pose challenges for state estimation.
  • Traditional filtering methods can lead to high communication burdens due to continuous data transmission.
  • Event-triggered mechanisms offer a solution to reduce network load in distributed systems.

Purpose of the Study:

  • To develop an event-triggered H∞ filtering strategy for delayed neural networks using sampled data.
  • To significantly reduce the information communication burden in networked systems.
  • To design a filter that guarantees asymptotic stability and H∞ performance.

Main Methods:

  • A novel event-triggered scheme based on current and past sampled data errors.
  • Construction of a Lyapunov-Krasovskii functional.
  • Application of reciprocally convex combination technique and Jensen's inequality.
  • Formulation of filter design using Linear Matrix Inequalities (LMIs).

Main Results:

  • Sufficient conditions for asymptotic stability of the filtering error system are derived.
  • The proposed event-triggered scheme effectively reduces data transmission.
  • H∞ performance analysis is integrated into the filter design.
  • The effectiveness of the method is validated through a numerical example.

Conclusions:

  • The proposed event-triggered H∞ filtering approach is effective for delayed neural networks.
  • The method achieves reduced communication load while maintaining system stability and performance.
  • LMIs provide a systematic way to design the H∞ filter based on stability conditions.