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Related Concept Videos

Radial System Protection01:23

Radial System Protection

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Radial systems employ time-delay overcurrent relays to reduce load interruptions. When a fault occurs, the nearest breaker opens first, while upstream breakers remain closed due to longer delay settings. This approach ensures minimal disruption to the rest of the system.
In a radial system with a fault downstream of the third breaker, ideally, only the third breaker will open, isolating the fault and interrupting the load connected beyond it. The second breaker has a longer delay setting,...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Multimedia Security Situation Prediction Based on Optimization of Radial Basis Function Neural Network Algorithm.

Gan Chen1

  • 1Guangzhou Institute of Technology, Guangzhou, Guangdong 510075, China.

Computational Intelligence and Neuroscience
|April 18, 2022
PubMed
Summary

This study introduces an improved network security situation prediction method using a generalized radial basis function (RBF) neural network. The enhanced model significantly reduces prediction errors, boosting network security awareness and active protection capabilities.

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Artificial Intelligence

Background:

  • Network situation awareness is crucial for proactive security.
  • Existing prediction methods face challenges in accuracy.
  • Accurate prediction enhances active network security protection.

Purpose of the Study:

  • To propose a novel network security situation prediction method.
  • To improve the accuracy of network situation awareness.
  • To enhance the active security protection of networks.

Main Methods:

  • Utilized a generalized radial basis function (RBF) neural network.
  • Employed K-means clustering to determine RBF parameters (center, expansion function).
  • Applied the least-mean-square algorithm for weight adjustment to establish nonlinear mapping for prediction.

Main Results:

  • The proposed method achieves more accurate situation prediction results.
  • The IAFSA-PSO-RBF model demonstrated reduced maximum relative error (by up to 32.98%) and minimum relative error (by up to 12.97%) compared to other models.
  • Achieved an average relative error of 5% and an accuracy improvement exceeding 5% over comparative models.

Conclusions:

  • The developed method effectively predicts network security situations.
  • The enhanced accuracy contributes to improved active security protection.
  • The model meets the stringent requirements for network security situation prediction.