
Fuzzy Systems
Time-Series Prediction using ANFIS in MATLAB
Build a lagged time-series predictor with ANFIS in MATLAB, using five delays, a global ice-volume example, and a split that preserves stored order.
Fuzzy systems describe knowledge with rules and degrees of membership instead of sharp yes or no values. They are useful for control, prediction and decisions when the data is uncertain.
These posts cover fuzzy inference, ANFIS, fuzzy clustering and fuzzy control, with MATLAB code that you can study and extend.

Fuzzy Systems
Build a lagged time-series predictor with ANFIS in MATLAB, using five delays, a global ice-volume example, and a split that preserves stored order.

Fuzzy Systems
Train ANFIS parameters in MATLAB with GA or PSO, using multiplier encoding, a training-RMSE objective, and a nonlinear-regression example.

Fuzzy Systems
Simulate fuzzy PID gain scheduling in MATLAB and Simulink with a two-input rule base, variable gains, and three plant examples.

Fuzzy Systems
Model nonlinear regression with ANFIS in MATLAB, including fuzzy rules, hybrid training, FIS generation methods, and an engine-data example.