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Convolutional Neural Network (CNN) model integrated with a Mamdani Fuzzy Inference System/30_days_of_Python #71
This project will demonstrates the creation and enhancement of a Convolutional Neural Network (CNN) for classifying synthetic data, formatted as 10x10 pixel 'images' into 10 distinct classes. The CNN model's output is further processed using a Mamdani Fuzzy Inference System to perform decision-making based on the model's confidence.
The project will be divided into several key stages:
Data Generation and Preprocessing: Creating synthetic data, reshaping, normalizing, and splitting into training and test sets.
Model Architecture: Designing a more complex CNN model to improve classification accuracy.
Training with Data Augmentation: Enhancing the model's generalization through data augmentation and advanced training techniques.
Evaluation: Assessing the model's performance on test data.
Integration with Fuzzy Logic: Implementing a Mamdani Fuzzy Inference System to make decisions based on the CNN's output.
jitacm
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4 months ago
Description
This project will demonstrates the creation and enhancement of a Convolutional Neural Network (CNN) for classifying synthetic data, formatted as 10x10 pixel 'images' into 10 distinct classes. The CNN model's output is further processed using a Mamdani Fuzzy Inference System to perform decision-making based on the model's confidence.
The project will be divided into several key stages:
Data Generation and Preprocessing: Creating synthetic data, reshaping, normalizing, and splitting into training and test sets. Model Architecture: Designing a more complex CNN model to improve classification accuracy. Training with Data Augmentation: Enhancing the model's generalization through data augmentation and advanced training techniques. Evaluation: Assessing the model's performance on test data. Integration with Fuzzy Logic: Implementing a Mamdani Fuzzy Inference System to make decisions based on the CNN's output.