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Event-triggered H∞ filter design for delayed neural network with quantization.

Jinliang Liu1, Jia Tang2, Shumin Fei3

  • 1College of Information Engineering, Nanjing University of Finance and Economics, Nanjing, Jiangsu 210023, PR China; School of Automation, Southeast University, Nanjing, Jiangsu 210096, PR China.

Neural Networks : the Official Journal of the International Neural Network Society
|July 27, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces an event-triggered communication scheme and logarithmic quantization for H∞ filter design in neural networks. This approach conserves communication resources and reduces data rates for improved system performance.

Keywords:
Event-triggered schemeExponential stabilityNeural networksQuantizationfilter

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Area of Science:

  • Control Systems Engineering
  • Networked Systems
  • Artificial Intelligence

Background:

  • Neural network systems require efficient data transmission and filtering for reliable operation.
  • Limited communication resources and data rates pose challenges in networked control systems.
  • Event-triggered communication and quantization are key strategies for resource efficiency.

Purpose of the Study:

  • To design an H∞ filter for neural network systems incorporating an event-triggered communication scheme and quantization.
  • To develop a novel event-triggered strategy for selective data transmission.
  • To address the impact of network constraints on filter performance.

Main Methods:

  • A new event-triggered communication scheme is proposed to optimize data transmission.
  • A logarithmic quantizer is employed to reduce data transmission rates.
  • Lyapunov functional and linear matrix inequality (LMI) techniques are utilized for stability analysis and filter design.

Main Results:

  • Delay-dependent stability conditions for the H∞ filter are derived.
  • Explicit expressions for the filter parameters are obtained using LMIs.
  • The effectiveness of the proposed method is validated through a numerical example.

Conclusions:

  • The developed event-triggered H∞ filter design effectively manages communication resources and data rates in neural network systems.
  • The proposed methods provide a robust framework for designing filters under network constraints.
  • The numerical results demonstrate the practical utility of the theoretical findings.