Intelligent Simulation of Children's Psychological Path Selection Based on Chaotic Neural Network Algorithm

Area of Science:

• Developmental Psychology
• Computational Neuroscience
• Artificial Intelligence

Background:

• Current intelligent simulation models for children's psychological path selection often overlook crucial influencing factors.
• This limitation hinders accurate prediction and guidance for children's developmental trajectories.

Purpose of the Study:

• To apply a chaotic neural network algorithm for enhanced intelligent simulation of children's psychological path selection.
• To develop a model that considers individual personality traits and regional variations in psychological path choices.

Main Methods:

• Development of an intelligent simulation model utilizing a chaotic neural network algorithm.
• Integration of visual analysis strategies to identify and analyze regional influencing factors.
• Experimental validation of the model's efficacy in classifying optimal psychological paths.

Main Results:

• The chaotic neural network-based model demonstrated superior classification performance compared to iterative loop algorithms.
• The model effectively identified optimal psychological paths by adapting to children's personality differences and regional classifications.
• Experimental results showed an improvement of at least 37% over traditional methods.

Conclusions:

• The chaotic neural network algorithm offers a robust and adaptive approach for simulating children's psychological path selection.
• This method provides more accurate and personalized insights into developmental choices, outperforming existing techniques.