Prediction of Employee Turnover Trend in MSMEs Based on Whale Alogorithm Optimized Elman Neural Network
Jiying Tian · 2023
The Elman Neural Network is known for its unique architecture, incorporating recurrent connections through a context layer to capture temporal dependencies and facilitate dynamic modeling. Its adaptability enables precise approximation of complex nonlinear mappings while demonstrating resilience to external noise and disturbances. This study focuses on enhancing the Elman Neural Network's performance through the application of the Whale Optimization Algorithm (WOA), refining initial weights and thresholds to minimize absolute errors and improve overall performance. Leveraging the WOA's robust search and optimization capabilities, the network efficiently adapts to complex datasets, ensuring heightened accuracy and reduced errors in predictive tasks. The paper introduces the Elman Neural Network's construction, emphasizing the significance of selecting optimal initial weights and thresholds. Additionally, it presents an optimization methodology based on the WOA, refining the network's functionality and reducing errors. What's more, the study demonstrates the practical application of the optimized Elman Neural Network in predicting employee turnover trends within MSMEs, leveraging historical statistical data to provide valuable insights for proactive management.