Final assessment of the model's efficiency in real-world classification tasks.
The report inside such a file focuses on improving . A Fuzzy Neural Network combines the human-like reasoning of fuzzy logic with the learning capabilities of neural networks. The "Regularized" aspect is the primary innovation, which:
Comparison tables showing performance against standard neural networks.
Mathematical framework of the Regularized Fuzzy Neural Network. How the system simplifies its own rules to save resources. Experimental Results
Uses mathematical penalties (like L1 or L2 regularization) to ensure the model performs well on new, unseen data rather than just "memorizing" training data.
Standard pattern classification benchmarks (e.g., Iris, Wine, or Breast Cancer datasets) used to test the model's accuracy.