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

• Published in: Computational intelligence and neuroscience Vol. 2021; no. 1; p. 5321153
• Main Author: Wang, Yue
• Format: Journal Article
• Language: English
• Published: United States Hindawi 2021; John Wiley & Sons, Inc
• Subjects: Accuracy; Algorithms; Analysis; Child; Children; Classification; Computer Simulation; Data analysis; Deep learning; Design; Energy consumption; Humans; Iterative methods; Neural networks; Neural Networks, Computer; Psychology; Regions; Research methodology; Simulation
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2021/5321153

In recent years, there are many problems in the study of intelligent simulation of children’s psychological path selection, among which the main problem is to ignore the factors of children’s psychological path selection. Based on this, this paper studies the application of chaotic neural network algorithm in children’s mental path selection. First, an intelligent simulation model for children’s mental path selection based on chaotic neural network algorithm is established; second, it will combine the network based on different types of visual analysis strategies. The model is used to analyze the influencing factors of children in different regions in the choice of psychological paths. Finally, experiments are designed to verify the actual application effect of the simulation model. The results show that compared with the current mainstream intelligent simulation methods with iterative loop algorithms as the core, it adopts the intelligent simulation model based on the chaotic neural network algorithm has a good classification effect. It can effectively select the optimal psychological path according to the differences in children’s personality and can adaptively classify children in different regions, and the experimental results are accurate. Compared with the traditional method, it is improved by at least 37%.