Development of a Brand Talent Demand Prediction Model Using Gray Neural Networks

A predictive model for company talent demand was developed utilizing Gray neural networks (GNNs). Based upon the principles of Gray system theory and neural network methodologies, the model integrates the robust forecasting capabilities of gradient projection memory (GPM) with the learning capacity of neural networks. The proposed model preprocesses historical data on company talent demand indicators including company visibility, market sentiment, and talent demographics. The model is trained with a neural network using the GPM's forecasts as inputs. The model is trained iteratively to optimize performance metrics such as mean squared error and accurately predict company talent demand indicators. Validation and evaluation with datasets were conducted for the model's predictive accuracy, robustness, and reliability. The effectiveness of the developed model in forecasting company talent demand was confirmed in strategic decision-making for talent management.