From Data to Decisions: Leveraging ANFIS for Adaptive Rule-Based Systems
Abstract
The Adaptive Neuro-Fuzzy Inference System (ANFIS) is a hybrid intelligent system that integrates the learning capabilities of neural networks with the transparent reasoning structure of fuzzy logic. Designed for continuous function approximation and decision-making tasks, ANFIS uses a data-driven approach to construct fuzzy if-then rules and adjust membership function parameters through learning algorithms such as backpropagation or hybrid optimization. This fusion allows ANFIS to effectively model complex, nonlinear systems while maintaining interpretability. By translating numerical data into linguistically interpretable rules, ANFIS offers a flexible framework capable of adapting to evolving data patterns. This paper explores the foundational structure of ANFIS, detailing its layered architecture, rule generation process, and learning mechanisms. The discussion highlights its suitability for data-driven environments where interpretability, adaptability, and predictive accuracy are essential in building intelligent decision support systems.
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