What isAdaptive Neuro Fuzzy Inference System (ANFIS)
A hybrid intelligent system that combines the learning capabilities of artificial neural networks and the rule-based reasoning of fuzzy logic. It is used for complex decision-making and modeling.
Adaptive Neuro-Fuzzy Inference Systems (ANFIS) are hybrid intelligent systems that integrate the strengths of artificial neural networks and fuzzy logic. They blend the learning capabilities of neural networks with the interpretability and linguistic reasoning of fuzzy logic, making them suitable for complex decision-making and modeling tasks.
Key Components of ANFIS
- **Fuzzy Rules:** ANFIS systems operate on a set of fuzzy IF-THEN rules, defining the relationships between inputs and outputs in a linguistic manner., **Membership Functions:** These functions define the degree of membership of input values to different fuzzy sets., **Inference Engine:** The engine combines the fuzzy rules and membership functions to perform inference and determine the output., **Learning Algorithm:** ANFIS uses a learning algorithm to adjust the parameters of the membership functions and fuzzy rules based on training data.
The learning algorithm, often based on gradient descent, refines the system's parameters to minimize errors between the predicted and actual outputs. This iterative process allows the ANFIS to adapt to new data and improve its performance.
ANFIS is particularly well-suited for problems with complex, non-linear relationships between inputs and outputs where traditional methods might struggle.