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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.
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Optimizing the Probabilistic Neural Network Model with the Improved Manta Ray Foraging Optimization Algorithm to

Xiyuan Liu1, Liying Wang1,2, Hongyan Yan1,2

  • 1School of Water Conservancy and Hydropower, Hebei University of Engineering, Handan 056038, China.

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Summary

An improved manta ray foraging optimization (ITMRFO) algorithm enhances probabilistic neural network (PNN) accuracy for identifying hydraulic turbine draft tube pressure fluctuations. This novel ITMRFO-PNN model offers superior performance in signal identification.

Keywords:
hydraulic turbinemanta ray foraging optimization algorithmpressure fluctuationprobabilistic neural networksignal identification

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Area of Science:

  • Fluid Mechanics
  • Signal Processing
  • Machine Learning

Background:

  • Accurate identification of pressure fluctuation signals in hydraulic turbine draft tubes is crucial for operational efficiency and safety.
  • Existing methods may suffer from limitations in accuracy and susceptibility to local optimization problems.

Purpose of the Study:

  • To develop an advanced method for improving the identification accuracy of pressure fluctuation signals in hydraulic turbine draft tubes.
  • To optimize the probabilistic neural network (PNN) using an enhanced metaheuristic algorithm.

Main Methods:

  • Feature extraction from vibration signals using discrete wavelet transform (DWT).
  • Automatic information classification via fuzzy c-means (FCM) clustering.
  • Development of an improved manta ray foraging optimization (ITMRFO) algorithm to address MRFO's local optimization issues.
  • Optimization of PNN smoothing factors using the ITMRFO algorithm, creating the ITMRFO-PNN model.

Main Results:

  • The ITMRFO algorithm demonstrated superiority over other algorithms on 23 test functions.
  • The ITMRFO-PNN model significantly outperformed standard PNN and MRFO-PNN models in identifying pressure fluctuation signals.
  • Evaluation metrics including accuracy, precision, recall, and F1-score confirmed the ITMRFO-PNN model's effectiveness.

Conclusions:

  • The proposed ITMRFO-PNN model provides a robust and effective solution for identifying pressure fluctuation signals in hydraulic turbine draft tubes.
  • This study offers a strong theoretical basis for enhancing the diagnostic capabilities in hydro-power engineering.