MATLAB Program for ANFIS Construction

This article primarily discusses how to construct ANFIS programs using MATLAB and explores their practical applications and theoretical significance. The ANFIS program can be employed for various tasks including data modeling, classification, and prediction, making it highly versatile. Key implementation aspects include using MATLAB's Fuzzy Logic Toolbox functions like genfis1 for initial FIS generation and anfis for training the adaptive network. The system combines fuzzy logic principles with neural network learning capabilities through a five-layer architecture: input layer, fuzzification layer, rule layer, normalization layer, and output layer. Additionally, this article examines the advantages and disadvantages of ANFIS programs, such as their ability to handle nonlinear systems versus potential overfitting issues, and suggests potential improvement methods like hybrid learning algorithms that combine gradient descent with least-squares estimation. The discussion aims to provide comprehensive information and insights for readers interested in this field.