Hi Vane,
I understand that you have not achieved satisfactory results in validation performance for your model because of high “Mean Squared Error (MSE).”
Here are some general suggestions that can help you reduce the “Mean Squared Error (MSE)” values for your “NARX” model:
- Increase training data: If possible, gather more training data to improve the model's ability to learn patterns and enhance its performance.
- Regularize the model: Apply regularization techniques such as L1 or L2 regularization to prevent overfitting and improve the model's generalization ability.
- Adjust the network architecture: Experiment with different network architectures by varying the number of neurons, layers, and delays.
- Normalize the data: Scale your input and output data to a similar range. Normalization prevents features with large values from dominating the training process and can improve the model's performance.
- Adjust training parameters: Try different learning rates, number of epochs, and batch sizes. Additionally, explore different optimization algorithms ('trainlm', 'trainbr', 'traingd') to see if they yield better results for your specific problem.
- Explore alternative models: If the NARX model is not providing satisfactory results, consider exploring other types of models that might be more suitable for your specific problem.
Please refer to the below mentioned documentations to know more about overfitting and regularization :
I hope you find this helpful.
Best Regards,
Lokesh
