Analysis of Chain Enterprise Management Mode Considering RBF Neural Network Knowledge Recognition Algorithm

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Rui Tian, Birong Xu


Chain enterprise management is a complex and dynamic process that requires sophisticated strategies to optimize efficiency and effectiveness. In this study, we propose an analysis of the chain enterprise management mode, focusing on the integration of the Radial Basis Function (RBF) neural network knowledge recognition algorithm. The RBF neural network is employed as a powerful tool for understanding and recognizing patterns within the vast amount of data generated by chain enterprises. This research aims to investigate the application of the RBF neural network in the context of chain enterprise management, considering its ability to recognize complex patterns and adapt to changing environments. The analysis involves examining various aspects of chain enterprise management, including supply chain optimization, customer relationship management, and operational efficiency. Through the utilization of the RBF neural network, we seek to enhance decision-making processes within chain enterprises by providing accurate and timely insights into operational dynamics. By integrating knowledge recognition algorithms into the management mode, we anticipate improvements in forecasting accuracy, risk assessment, and strategic planning. Furthermore, this study evaluates the potential challenges and limitations associated with implementing RBF neural networks in chain enterprise management. Factors such as data quality, computational resources, and algorithmic complexity are considered to ensure practical feasibility and scalability. By combining theoretical analysis with practical insights, we aim to provide valuable guidance for decision-makers seeking to enhance the performance of their chain enterprises in an increasingly competitive business environment.

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