Adaptive Neuro-Fuzzy Inference Systems (ANFIS)

This document describes ANFIS (Adaptive Neuro-Fuzzy Inference Systems), a computational model that mimics human brain functionality represented as mathematical models. ANFIS combines the advantages of both neural networks and fuzzy systems, employing a cognitive approach to problem-solving. The system typically implements a five-layer architecture where the first layer handles input fuzzification, subsequent layers perform rule evaluation and normalization, and the final output layer produces defuzzified results.

ANFIS operates based on supervised learning methodology, using datasets to train the system and adjust parameters to generate correct outputs for specific inputs. The learning process typically employs hybrid optimization algorithms combining least-squares estimation with backpropagation gradient descent. ANFIS can extract data features and perform classification and prediction based on training data through its adaptive rule base and membership function optimization. Key functions often include "anfis" for training, "evalfis" for inference, and parameter tuning methods for system optimization.

ANFIS finds applications across numerous domains including control engineering, finance, healthcare, transportation, and energy systems. Implementation typically involves MATLAB's Fuzzy Logic Toolbox or Python libraries like scikit-fuzzy. Using ANFIS facilitates data analysis and predictive modeling, enabling more accurate forecasts while handling large datasets with high-speed, efficient processing capabilities through its parallel network structure.

This concludes the explanation of ANFIS systems.