A multi-agent knowledge integration process for enterprise management innovation from the perspective of neural network

Abstract

Because the innovation level of enterprise clusters in various regions of China is generally low, this research is focused on the process of knowledge integration of regional innovation subjects. We research, explore and analyze the different connection states between nodes and their impact on the knowledge symbiosis or knowledge spillover ability of the entire innovation network, learn from the characteristics of neurons in the neural network and the information transmission model to investigate the connection between various nodes in innovation network. We then determine the knowledge association mechanism and transmission relationship, and then analyze the trigger conditions of fusion and the transformation model of innovative knowledge flow under this condition, laying the foundation for further theoretical or practical research. Second, we built a model of the knowledge transfer connection that will be used in the innovation process. and select a path of knowledge transfer. Based on the mechanism of multi-agent innovation, we analyzed the incentive relationship of knowledge transfer in the innovation process, constructed the principles of knowledge transfer, analyzed the mutual transfer relationship between different innovation nodes, and analyze the simulation innovation network through certain examples. The knowledge fusion process in China lays the foundation for the improvement of the overall collaborative innovation level of the regional multi-agent innovation network. From the overall structure of the article, this research analyzes the regional innovation network from a new perspective on the basis of domestic and foreign research in knowledge flow management and control technology, knowledge exchange under social networks, and neural network optimization algorithms. The process of knowledge symbiosis or knowledge spillover in the process of innovation, exploring the incentive relationship of knowledge transfer throughout the primary parts innovation process, optimizing the degree of connection or relationship between different main agents, optimizing the knowledge exchange relationship from one node in the innovation network to another and improving the collaborative innovation of the regional mesh lay a theoretical foundation for the level.