Clustering Analysis Algorithm of Volleyball Simulation Based on Radial Fuzzy Neural Network

Area of Science:

• Sports Science
• Artificial Intelligence
• Data Analysis

Background:

• Volleyball analysis faces challenges with existing models.
• Radial fuzzy neural network theory offers potential for improved performance monitoring.

Purpose of the Study:

• To develop an optimized simulation clustering algorithm for volleyball analysis.
• To create an accurate optimization model describing the entire volleyball process.

Main Methods:

• Utilized radial fuzzy neural network theory and an optimized simulation clustering analysis algorithm.
• Analyzed feature weights of nodes at different stages to construct an optimal radial fuzzy neural network.
• Developed and verified an optimization model against the original model.

Main Results:

• The optimization model accurately describes the entire volleyball process, unlike the original model which only covers the initial stage.
• The optimized algorithm demonstrates superior performance in reflecting beam trends compared to MPDR and MVDR algorithms.
• Model indexes showed convergence below standard values after 30 iterations.

Conclusions:

• The developed optimization model provides a robust framework for analyzing and predicting volleyball performance.
• This approach offers theoretical support for applying simulation clustering algorithms in volleyball.
• The findings can guide volleyball training and development.