Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same journal

RETRACTION: Real-Time Modulation of Physical Training Intensity Based on Wavelet Recursive Fuzzy Neural Networks.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Multidimensional Heterogeneous Network Link Adaptation Based on Mobile Environment.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Intangible Cultural Heritage Reproduction and Revitalization: Value Feedback, Practice, and Exploration Based on the IPA Model.

Computational intelligence and neuroscience·2026

Related Experiment Video

Updated: Oct 17, 2025

Designing and Implementing Nervous System Simulations on LEGO Robots
10:34

Designing and Implementing Nervous System Simulations on LEGO Robots

Published on: May 25, 2013

15.2K

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

Yue Wang1

  • 1School of Education, Zhongyuan Institute of Science and Technology, Zhengzhou, Henan Province 450046, China.

Computational Intelligence and Neuroscience
|October 11, 2021
PubMed
Summary

This study introduces a chaotic neural network algorithm for intelligent simulation of children's psychological path selection. The new model effectively classifies optimal paths based on personality and regional differences, improving accuracy by over 37%.

More Related Videos

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.9K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.5K

Related Experiment Videos

Last Updated: Oct 17, 2025

Designing and Implementing Nervous System Simulations on LEGO Robots
10:34

Designing and Implementing Nervous System Simulations on LEGO Robots

Published on: May 25, 2013

15.2K
The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.9K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.5K

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.