Grey Neural Network Prediction Algorithm

This paper presents a prediction algorithm based on Grey Neural Network, where cumulative data summation is utilized as network input. The algorithm performs effective forecasting and generates prediction results through sophisticated data processing and analysis. The implementation typically involves two main components: grey system modeling (GM(1,1)) for data preprocessing and neural network for pattern recognition. Key algorithmic steps include: 1) Data accumulation generation through cumulative sum operations, 2) Grey differential equation solving using least squares estimation, 3) Neural network training with backpropagation optimization. This approach enables better understanding and prediction of dataset trends and patterns, providing valuable information for decision-making and planning. Furthermore, the algorithm can be adjusted and optimized according to specific requirements - such as modifying network architecture (number of hidden layers) or tuning grey parameters - to enhance prediction accuracy and reliability. The code structure typically includes functions for data preprocessing, grey modeling, network training, and prediction validation. Overall, the Grey Neural Network-based prediction algorithm serves as a powerful and effective tool with broad applications across various domains including energy forecasting, economic prediction, and industrial analytics.