How LSTMs and GRUs Improve RNNs (and Why Elman Is the Base)
Classic RNNs (like the Elman network) introduced recurrence but struggled with vanishing gradients, making long‑term memory hard. LSTMs and GRUs solve this by adding gates that control what to remember and what to forget.
Elman as the foundation
The Elman network is the simplest RNN:
- It passes the previous hidden state into the next step.
- That recurrence is the core idea behind LSTMs and GRUs.
LSTMs and GRUs keep the same recurrence structure, but add learned gates to preserve memory over longer sequences.
How LSTM improves RNNs
LSTMs add a cell state and three gates:
- Forget gate: decides what to drop from memory.
- Input gate: decides what to store.
- Output gate: decides what to expose.
This allows the network to keep information for many time steps without gradients vanishing.
How GRU improves RNNs
GRUs simplify LSTMs with two gates:
- Update gate: mixes old and new information.
- Reset gate: clears irrelevant past context.
GRUs are simpler and often faster, while still handling long‑term dependencies better than Elman RNNs.
Where they are used
- NLP: language modeling, translation, speech recognition.
- Time‑series forecasting: demand prediction, anomaly detection.
- Signals: sensor streams, audio processing, finance.
Quick comparison
| Model | Memory length | Complexity | Notes |
|---|---|---|---|
| Elman RNN | Short | Low | Simple, but vanishing gradients |
| LSTM | Long | High | Best long‑term memory, heavier compute |
| GRU | Long | Medium | Faster than LSTM, fewer parameters |
Summary
Elman RNNs introduced recurrence, but LSTMs and GRUs made it practical for long sequences. If you need long‑term memory, LSTMs or GRUs are usually better choices; if your sequences are short or compute is limited, Elman‑style RNNs can still work.