Radial Neural Network Processing Applied to Athlete's Personalized Psychological Regulation Detection System

Area of Science:

• Artificial Intelligence
• Computational Neuroscience
• Machine Learning

Background:

• Traditional methods struggle with complex pattern recognition and motion control tasks.
• BP neural networks often face challenges with regional minima, leading to slow training and inefficiency.
• Radial Basis Function (RBF) networks, or radial neural networks, offer enhanced capabilities in pattern recognition and personalized customization.

Purpose of the Study:

• To evaluate the effectiveness of radial neural networks in personalized regulation and psychological adjustment systems.
• To compare the performance of radial neural networks against traditional BP networks in specific application scenarios.
• To determine the potential of radial neural networks for improving athlete performance and privacy.

Main Methods:

• Implementation of a radial neural network for personalized regulation and psychological adjustment detection.
• Comparative analysis of radial neural networks against traditional BP networks.
• Evaluation of key performance metrics including analysis speed, privacy protection, and accuracy of psychological control schemes.

Main Results:

• Radial neural networks demonstrated a 20% improvement in analysis speed compared to classic networks.
• Achieved a 99% degree of privacy protection for athletes, nearing full value.
• Improved the accuracy of psychological control schemes by approximately 15%.

Conclusions:

• Radial neural networks are an ideal choice for psychological adjustment detection systems due to their efficiency and accuracy.
• The enhanced performance of radial neural networks supports personalized regulation and optimal athlete states.
• RBF networks provide superior capabilities in data handling, pattern recognition, and customized solutions.