COMPARATIVE STUDY OF SURROGATE TECHNIQUES FOR HYPERPARAMETER OPTIMIZATION IN RECURRENT NEURAL NETWORK
Long Short-Term Memory (LSTM) models are a type of recurrent neural network (RNN) well-suited for tasks requiring the model to remember long-term dependencies. This makes them a promising approach for ET rate estimation, as ET is a process that is influenced by various factors that may occur over lo...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
2023
|
| Subjects: | |
| Online Access: | http://utpedia.utp.edu.my/id/eprint/24854/1/2023_PhD%20in%20IT_thesis%20submission_1900298_Muhammad%20Zahid%20bin%20Hilmi.pdf http://utpedia.utp.edu.my/id/eprint/24854/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Long Short-Term Memory (LSTM) models are a type of recurrent neural network (RNN) well-suited for tasks requiring the model to remember long-term dependencies. This makes them a promising approach for ET rate estimation, as ET is a process that is influenced by various factors that may occur over long periods. |
|---|