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Related Concept Videos

Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Polynomials are algebraic expressions of terms with variables raised to non-negative integer powers. A central aspect of analyzing polynomial functions is determining their real zeros—values of the variable for which the polynomial evaluates to zero. These values represent the x-intercepts of the polynomial’s graph.The Rational Zeros Theorem lists possible rational solutions for a polynomial equation with integer coefficients. If f(x)=anxn+....+a0​, then every rational zero is of the form p/q​,...
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Updated: May 14, 2026

A Protocol for Real-time 3D Single Particle Tracking
10:16

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

PAC-ZNN for Robust Target Tracking in WSNs Against Complex Polynomial Noise.

Ziying Zhan1, Zhiyuan Song1, Songjie Huang1

  • 1School of Electronic and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China.

Sensors (Basel, Switzerland)
|May 13, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Polynomial Anti-Noise Compensation Zeroing Neural Network (PAC-ZNN) for wireless sensor networks. The PAC-ZNN enhances localization accuracy and stability in noisy environments.

Keywords:
nonlinear activation functionpolynomial anti-noise compensationwireless sensor networkszeroing neural network

Related Experiment Videos

Last Updated: May 14, 2026

A Protocol for Real-time 3D Single Particle Tracking
10:16

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

Area of Science:

  • Wireless Sensor Networks (WSNs)
  • Localization Algorithms
  • Neural Network Applications

Background:

  • Traditional Angle of Arrival (AOA) and Time Difference of Arrival (TDOA) localization systems in WSNs suffer performance degradation due to high-order time-varying noise.
  • Existing Zeroing Neural Networks (ZNNs) lack sufficient robustness against such noise, impacting convergence stability and accuracy.

Purpose of the Study:

  • To develop a more robust ZNN model for dynamic localization tasks in WSNs.
  • To improve the anti-noise capability, convergence efficiency, and localization precision under challenging noise conditions.

Main Methods:

  • Proposed a Polynomial Anti-Noise Compensation ZNN (PAC-ZNN) model.
  • Incorporated a Polynomial Anti-Noise Compensation (PAC) term to mitigate cumulative high-order noise effects.
  • Utilized a Logarithmic Mapping Activation Function (LMAF) to enhance convergence speed and stability.
  • Rigorous theoretical validation using Lyapunov stability theory.

Main Results:

  • The PAC-ZNN demonstrated superior anti-noise capability compared to traditional ZNNs.
  • Achieved enhanced convergence efficiency and localization precision in dynamic AOA and TDOA localization tasks.
  • Maintained robust tracking performance even in complex multipath environments.

Conclusions:

  • The PAC-ZNN offers significant improvements in robustness and accuracy for WSN localization under high-order noise.
  • The proposed model shows strong practical application value for real-world WSN deployment.