Shengyi Lin
ABSTRACT
This paper establishes a comprehensive framework for the Generalized Regression Neural Network (GRNN) and focuses on optimizing its functionality. GRNN, recognized for its robustness in handling limited or noisy data, plays a significant role in addressing real-world problems characterized by unstable and uncertain data. The study emphasizes the refinement of GRNN's underlying theory, highlighting the integration of the Sparrow Search Algorithm (SSA) to optimize the network's smoothing parameter. The paper delves into the construction and optimization of the GRNN model, emphasizing the development of its essential architectural layers. Finally, it showcases the practical implications of the optimized GRNN model, demonstrating its application in predicting enterprise talent demand within the context of this study.
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Index Terms
- Enterprise Talent Demand Prediction Model Based on Optimized Generalized Regression Neural Network
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