Neuro-Fuzzy Systems combine the learning capabilities of neural networks with the reasoning abilities of fuzzy logic. This integration allows these systems to handle uncertainty and imprecision in data, making them effective for complex decision-making tasks. They learn from examples and can adapt to new information, improving their performance over time. These systems are widely used in various applications, including control systems, pattern recognition, and data classification. By leveraging both neural and fuzzy techniques, they can model human-like reasoning, making them suitable for tasks that require flexibility and adaptability in uncertain environments.