Fuzzy Neural Network for Predicting Foundation Settlement

This article demonstrates how to implement fuzzy neural networks for predicting foundation settlement. Fuzzy neural networks combine grey system theory with neural network technology, creating a predictive model capable of handling incomplete input data and uncertainties. The provided source code includes key functions for data preprocessing, network initialization, and training algorithms. You'll learn practical implementation techniques for data preparation, model training with backpropagation optimization, and parameter tuning strategies. The code structure features modular design with separate functions for membership calculation, rule base implementation, and defuzzification processes. We provide detailed explanations on adapting the model architecture, adjusting learning rates, and interpreting prediction results. By following this tutorial, you can effectively integrate fuzzy neural networks into your projects and achieve accurate settlement predictions while understanding the underlying algorithmic mechanisms.