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Wavelet neural network using multiple wavelet functions in target threat assessment.

Gaige Wang1, Lihong Guo, Hong Duan

  • 1Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.

Thescientificworldjournal
|March 20, 2013
PubMed
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This study introduces a novel wavelet neural network (MWFWNN) for accurate aerial combat target threat assessment. The proposed method optimizes wavelet function selection, outperforming existing models.

Area of Science:

  • Artificial Intelligence
  • Neural Networks
  • Signal Processing

Background:

  • Target threat assessment is crucial in collaborative aerial combat.
  • Existing methods face challenges in accuracy and usefulness.
  • Wavelet neural networks (WNNs) offer potential but require careful wavelet function selection.

Purpose of the Study:

  • To develop an improved target threat assessment model for aerial combat.
  • To address the challenge of selecting optimal wavelet functions for WNNs.
  • To introduce a novel MWFWNN network for enhanced threat assessment accuracy.

Main Methods:

  • A wavelet mother function selection algorithm minimizing mean squared error was developed.
  • A wavelet function library was established for network construction.

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  • The MWFWNN network was built by selecting the optimal wavelet function based on minimum mean squared error.

    • Main Results:

    • The MWFWNN network achieved a mean squared error of 1.23 × 10⁻³.
    • Performance was superior to Wavelet Neural Network (WNN), Backpropagation (BP), and Particle Swarm Optimization-Support Vector Machine (PSO-SVM).
    • The model demonstrated strong predictive ability for target threat assessment.

    Conclusions:

    • The proposed MWFWNN network effectively enhances target threat assessment in aerial combat.
    • The wavelet function selection algorithm improves WNN performance.
    • The model provides a quick and accurate solution for threat assessment challenges.