Difference Analysis of Regional Economic Development Based on the SOM Neural Network with the Hybrid Genetic Algorithm

• Published in: Computational intelligence and neuroscience Vol. 2021; no. 1; p. 6734345
• Main Authors: Cai, Ying, Wang, Xu, Xiong, LiRan
• Format: Journal Article
• Language: English
• Published: United States Hindawi 2021; Hindawi Limited
• Subjects: Algorithms; Brain research; China; Cluster Analysis; Clustering; Data Mining; Decision making; Economic analysis; Economic Development; Economic growth; Economic structure; Empirical analysis; Feature maps; Genetic algorithms; Nervous system; Neural networks; Neural Networks, Computer; Regional development; Regions; Self organizing maps; Spatial distribution; China
• ISSN: 1687-5265; 1687-5273
• DOI: 10.1155/2021/6734345

Since the reform and opening up, China’s regional economy has developed rapidly. However, due to different starting points of economic development caused by the traditional distribution of productive forces and the differences in regions, resources, technologies, and policies, the level of economic development in different regions is uneven. Clustering analysis is a data mining method that clusters or classifies entities according to their characteristics and then discovers the whole spatial distribution law of datasets and typical patterns. It is of great significance to classify, compare, and study the economic development level of different regions in order to formulate the regional economic development strategy. In this paper, a self-organizing feature map (SOM) neural network with the hybrid genetic algorithm is used to cluster the differences of regional economic development, the clustering results are evaluated, and the empirical results are good. From this, some meaningful conclusions can be drawn, which can provide reference for the decision-making of coordinating regional economic development.