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Related Experiment Video

Updated: Apr 13, 2026

An Automated System for Sound Localization Testing in Hearing-Impaired Listeners
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A noise-resilient distributed recurrent neural network for multi-agent consensus control and acoustic source

Lei Jia1, Weibao Xiong1, Lin Xiao2

  • 1College of Computer Science (College of Software), College of Artificial Intelligence, Inner Mongolia University, Hohhot, 010021, China.

Neural Networks : the Official Journal of the International Neural Network Society
|April 11, 2026
PubMed
Summary

This study introduces a noise-resilient distributed recurrent neural network (NDRNN) for multi-agent systems. The NDRNN improves consensus control and acoustic source localization accuracy in noisy environments, offering faster convergence and reduced errors.

Keywords:
Acoustic source localizationDistributed recurrent neural networksMulti-agent consensusPeriodic noise signalRobustness

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Area of Science:

  • Robotics and Control Systems
  • Artificial Intelligence
  • Signal Processing

Background:

  • Multi-agent systems (MASs) face challenges in consensus control and acoustic source localization due to noise sensitivity.
  • Recurrent Neural Networks (RNNs) show promise but degrade in accuracy under noise, with limited research on disturbed MASs.

Purpose of the Study:

  • To develop a noise-resilient distributed RNN (NDRNN) for enhanced MAS performance.
  • To create a consensus control protocol (NDRNN-CP) and an acoustic source localization solver (NDRNN-S) using the NDRNN framework.

Main Methods:

  • Designed a noise-resilient distributed RNN (NDRNN) incorporating adaptive noise variation learning via a time-delay mechanism.
  • Developed an optimized activation function for accelerated convergence within the NDRNN.
  • Proposed NDRNN-CP for multi-agent consensus and NDRNN-S for distributed acoustic source localization.

Main Results:

  • NDRNN-CP ensures global stability, robustness against periodic/stochastic disturbances, and predefined-time convergence.
  • NDRNN-S maintains high accuracy in noisy multi-agent acoustic source localization.
  • Simulations show NDRNN-CP and NDRNN-S outperform conventional DRNN methods with faster convergence and lower steady-state errors.

Conclusions:

  • The proposed NDRNN framework effectively addresses noise interference in MAS tasks.
  • NDRNN-CP and NDRNN-S demonstrate significant improvements in consensus control and acoustic source localization.
  • The approach is effective and broadly applicable to various noisy multi-agent scenarios.