ANFIS Fuzzy Neural Network for Chaotic Time Series Prediction

This paper presents a source program implementation utilizing Adaptive Neuro-Fuzzy Inference System (ANFIS) for chaotic time series prediction. The methodology employs ANFIS modeling to analyze and forecast chaotic time series patterns. Through systematic modeling and training of chaotic time series data, the approach enables better understanding and prediction of future development trends. The implementation typically involves key components such as data preprocessing, fuzzy rule generation, membership function optimization using hybrid learning algorithms (combining least-squares and backpropagation methods), and iterative parameter tuning. This method demonstrates considerable accuracy and practicality, featuring applications across various domains including financial forecasting, weather prediction, and complex system analysis. The code structure generally includes time series normalization, input-output data partitioning, ANFIS architecture configuration with appropriate membership functions, and performance evaluation metrics calculation for prediction accuracy assessment.