Adaptive Neuro-Fuzzy Inference System (ANFIS) Model Development

The Adaptive Neuro-Fuzzy Inference System (ANFIS) is an intelligent modeling approach that combines the strengths of fuzzy logic and neural networks, effectively enhancing model accuracy and generalization capabilities.

ANFIS's core concept involves preprocessing input data through a fuzzy rule base, followed by optimizing model parameters using the adaptive learning capabilities of neural networks. This hybrid structure preserves fuzzy logic's ability to handle uncertain information while leveraging neural networks for precise fitting of nonlinear relationships.

The model development process typically includes these key steps:

Fuzzification: Converting input variables into fuzzy sets using membership functions (e.g., Gaussian, triangular, or trapezoidal functions implemented via MATLAB's mf functions) to quantitatively express uncertain information.

Rule Base Construction: Generating "IF-THEN" fuzzy rules based on expert knowledge or data-driven methods (commonly automated using clustering algorithms like FCM), forming the initial inference framework.

Parameter Optimization: Automatically adjusting antecedent (membership function parameters) and consequent (rule output) parameters using neural network backpropagation or hybrid learning algorithms (implemented through gradient descent or least-squares estimation in ANFIS training).

Defuzzification: Converting fuzzy inference results into crisp output values (typically using methods like centroid calculation) to complete prediction or classification tasks.

ANFIS's accuracy advantages stem from three aspects: fuzzy rules' ability to integrate domain knowledge, neural networks' adaptive optimization characteristics, and the hybrid structure's enhanced capability to fit nonlinear systems. In practical applications, through appropriate membership function design and optimized training strategies (including parameter initialization and learning rate adjustment), ANFIS often achieves superior modeling accuracy compared to traditional methods.