Analysis of Industry Convergence Based on Improved Neural Network

Abstract

Economic growth in the information age is no longer a stage driven by unipolarity. It has entered a multi-polar driving stage characterized by integration, fusion, and integrated development on a larger scale between regions, and the trend of group competition with urban agglomerations as carriers has become increasingly obvious. This paper improves the neural network algorithm based on the needs of industrial economic integration in the digital age, and proposes an industry convergence analysis model based on the improved neural network algorithm. Moreover, this article combines industry models to analyze actual needs and constructs an industry convergence analysis model based on improved neural networks, and analyzes the integration of different industries. In addition, this article conducts experiments through multiple sets of data, and combines the neural network model of this article to conduct research. Through experimental research, we know that the model constructed in this paper can play an important role in the analysis of industry convergence.

A comprehensive performance evaluation algorithm for substation secondary equipment: An improved analytic hierarchy process entropy weight and learning vector quantization neural network approach

“…Meanwhile, Ma et al. [11] improved the traditional AHP method, and although more reasonable results of indicator weight assignment were obtained, its method still has some limitations. However, the above studies on AHP or improved AHP methods mainly consider the influence of human experience factors in the weight allocation process, but often neglect the consideration of objective factors, which limits the applicability and accuracy of these research methods.…”