How to create a "soft sensor" based on an LSTM network
You'll need to take the following steps:
Collect data and preprocess
It collects data from processes and systems that you want to monitor.
It is to preprocess it for use in an LSTM network.
This includes data cleaning, normalization, and network understanding.
Format and more.
Define the architecture of the LSTM network
This is the number of layers, the number of neurons in each layer
and other hyperparameters to be used in the network.
Learning an LSTM Network
After preprocessing the data and defining the network architecture,
Use that data to train the LSTM network.
This involves feeding the network input data and the corresponding output data.
It involves adjusting the weights and biases of the network so that accurate predictions can be made.
Test the LSTM network
Once the network has been trained, test it on a different dataset.
Evaluate its performance.
Implement an LSTM network
When you're done learning and testing your network
It can be used as a "soft sensor" to make predictions about processes and systems.
It is important to note that this is a high-level overview and that each step has many details and considerations.
Also, depending on the complexity of the data, to improve the performance of the LSTM network
Feature engineering, regularization, ensemble method, etc.
You may also need to use more advanced technology.