Hi Álvaro
Improving the performance of your neural network in MATLAB when using nntraintool can involves several strategies. Here are some suggestions to help you achieve better convergence and performance:
Data Preprocessing:
- Normalization: Ensure your input and target data are normalized. This can help the network learn more effectively.
- Data Quality: Check for outliers or errors in your dataset that could be affecting learning.
Network Architecture:
- Hidden Layers and Neurons: Experiment with different numbers of hidden layers and neurons. More complex models might require deeper networks, but be cautious of overfitting.
- Activation Functions: Try different activation functions for hidden layers, such as ReLU, sigmoid, or tanh, to see which works best for your problem.
Validation and Early Stopping:
- Validation Set: Ensure your validation set is representative of your data. Consider using cross-validation to get a more reliable estimate of the network's performance.
- Early Stopping Patience: Increase the number of validation checks before stopping to give the network more chances to improve.
Initialization:
- Weight Initialization: Use a different weight initialization strategy, such as Xavier or He initialization, which can help with better convergence.
Increase Epochs:
- Maximum Epochs: If the network is stopping due to reaching the maximum number of epochs, consider increasing this limit.
By systematically experimenting with these strategies and monitoring the results, you should be able to improve the performance of your neural network for solving your dynamic economic model.
Hope this helps!
