In this article, we explore how we utilized ChatGPT, specifically the GPT-4 model, to identify the best artificial neural network library and model for a system with 12 sensor input features and a time-domain dataset, accompanied by a 4-sensor label output with time-domain values. Not only did GPT-4 provide valuable insights, but it also assisted in debugging the code when needed.
Finding the Right Model:
Our primary goal was to find a suitable model that could handle the multivariate time series data from our sensors effectively. With the help of GPT-4, we decided to use scikit-learn, a popular Python library for machine learning. We chose the RandomForestRegressor model wrapped in a MultiOutputRegressor for our specific use case, as it can capture non-linear relationships between input features and output labels.
Training the Model and Evaluating Performance:
We trained our model with a dataset containing 20 minutes of data, which included noise from the Open Muscle system version 5.3.0. Despite the noise, our model achieved a Mean Absolute Error (MAE) of between 25 and 50, with output label ranges from 5200 to 5500. This result roughly translates to an error rate of around 12%, indicating the model’s reasonable performance in capturing the underlying patterns in the data.
Real-Time Predictions with Pickle:
To utilize our trained model for real-time predictions, we saved it using Python’s pickle library. This allowed us to seamlessly load the model into our application and feed it live data from the sensors. As a result, we were able to compare the real output and the predicted output, enabling us to assess our model’s performance in real-time.
Next Steps for Improvement:
Although our current model performs reasonably well, there are several steps we can take to further enhance its performance and the quality of our sensor data:
- Improve the signal quality of the Open Muscle system by upgrading from 12-bit to 24-bit ADCs. This will increase the resolution and accuracy of our sensor readings.
- Implement proper shielding for the PCBs to reduce electromagnetic interference and noise in the sensor data.
- Train the model with a larger dataset to better capture the underlying patterns and relationships in the data.
Using ChatGPT, specifically the GPT-4 model, we successfully identified and implemented an artificial neural network model to handle our time-domain sensor system. With real-time predictions in place, we can now focus on improving the signal quality and training our model with more data to achieve even better performance.