MATLAB Implementation of ANFIS Construction: Adaptive Neuro-Fuzzy Inference System

ANFIS (Adaptive Neuro-Fuzzy Inference System) is a hybrid intelligent system that combines the advantages of fuzzy logic and neural networks, commonly used for modeling and controlling complex systems. In the MATLAB environment, constructing an ANFIS model can be efficiently implemented using the built-in Fuzzy Logic Toolbox.

The core concept of ANFIS utilizes the self-learning capability of neural networks to automatically adjust parameters of the fuzzy system. A typical ANFIS structure consists of five layers: input fuzzification layer, rule layer, normalization layer, conclusion layer, and output aggregation layer. In MATLAB, the establishment of these hierarchical structures is encapsulated into concise function calls, primarily using functions like genfis() for initial fuzzy inference system generation and anfis() for training the system.

The construction process typically begins with data preparation, requiring both training and testing datasets. Initialization of fuzzy rules can be achieved through methods like grid partitioning or subtractive clustering. MATLAB provides corresponding functions such as genfis1() for grid partitioning and genfis2() for subtractive clustering to automate this step. During the system training phase, a hybrid learning algorithm combining least squares estimation and backpropagation algorithm is employed to adjust both premise parameters (membership function parameters) and consequent parameters (rule output parameters).

By adjusting training parameters such as number of epochs (iterations), error goal, and step size, model performance can be optimized. The anfis() function accepts these parameters as input arguments and returns the trained FIS structure. After training completion, the model's generalization capability can be validated using test data through prediction functions like evalfis(). ANFIS models find wide applications in prediction, classification, and control domains, with their main advantage lying in handling uncertainty and nonlinear problems effectively through adaptive parameter tuning and fuzzy rule optimization.