Historical database training method for neural networks

A method for training a neural network using real-time data, comprising the steps of:

• (1) detecting first lab data;
• (2) training or retraining a neural network, when said first lab data is detected by step (1), by using a first training set based on said first lab data;
• (3) detecting second lab data;
• (4) training or retraining said neural network, when said second lab data is detected by step (3), by using a second training set based on said second lab data, and by using said first training set;
• (5) detecting third lab data;
• (6) training or retraining said neural network, when said third lab data is detected by step (5), by using a third training set based on said third lab data, and using said second training set, without using said first training set;
• wherein at least one of steps (2), (4), and (6) comprises the steps of,
• (a) retrieving, from an historical database, training input data having a timestamp(s);
• (b) selecting a training input data time based on said timestamp(s);
• and
• (c) retrieving an input data indicated by said training input data time.

A method for training a neural network, comprising the steps of:

• (1) constructing a stack containing at least two training sets;
• (2) training or retraining the neural network using said at least two training sets in said stack;
• (3) constructing a new training set and replacing an oldest training set in said stack with said new training set;
• and
• (4) repeating steps (2) and (3) at least once;
• wherein at least one of steps (1) and (3) comprises the steps of,
• (a) retrieving, form an historical database, training input data having a timestamp(s);
• (b) selecting a training input data time based on said timestamp(s);
• and
• (c) retrieving an input data indicated by said training input data time.

A method for training a neural network using data from a physical process, comprising the steps of:

• (1) operating the physical process and measuring the same to produce a first lab data, a second lab data, and a third lab data;
• (2) training a neural network using a first training set based on first lab data;
• (3) training or retraining said neural network using a second training set based on second lab data, and using said first training set;
• and
• (4) training or retraining;
• said neural network using a third training set based on third lab data, and using said second training set, without using said first training set;
• wherein at least one of steps (2), (3), and (4) comprises the steps of,
• (a) retrieving, from an historical database, training input data having a timestamp(s);
• (b) selecting a training input data time based on said timestamp(s);
• and
• (c) retrieving an input data indicated by said training input data time.

A method for training a neural network for process control, comprising the steps of:

• (1) training a neural network using a first training set based on first lab data;
• (2) training or retraining said neural network using a second training set based on second lab data, and using said first training set;
• (3) training or retraining said neural network using a third training set based on third lab data, and using said second training set, without using said first training set;
• (4) predicting, using said neural network, a first output data using a first input data;
• and
• (5) changing a physical state of an actuator in accordance with said first output data;
• wherein at least one of steps (1), (2), and (3) comprises the steps of,
• (a) retrieving, front an historical database, training input data having a timestamp(s);
• (b) selecting a training input data time based on said timestamp(s);
• and
• (c) retrieving an input data indicated by said training input data time.

A method for training a neural network for process control using real-time data, comprising the steps of:

• ((1) detecting first lab data;
• (2) training or retraining a neural network, when said first lab data is detected by step (1), by using a first training set based on said first lab data;
• (3) detecting second lab data;
• (4) training or retraining said neural network, when said second lab data is detected by step (3), by using a second training set based on said second lab data, and by using a second training set based on second lab data, and by using said first training set;
• (5) detecting third lab data;
• (6) training or retraining said neural network, when said third lab data is detected in step (5), by using a third training set based on said second lab data, and by using a third training set based on third lab data, and using said second training set, without using said first training set;
• (7) predicting, using said neural network, a first output data using a first input data;
• and
• (8) changing a physical state of an actuator in accordance with said first output data;
• wherein at least one of steps (2), (4), and (6) comprises the steps of,
• (a) retrieving, from an historical database, training input data having a timestamp(s);
• (b) selecting a training output data time based on said timestamp(s);
• and
• (c) retrieving an input data indicated by said training input data time.

A method for training a neural network using real-time data from a physical process, comprising the steps of:

• (1) operating the physical process and measuring the same to produce a first lab data, a second lab data, and a third lab data;
• (2) detecting first lab data;
• (3) training or retraining a neural network, when said first lab data is detected by step (2), by using a first training set based on said first lab data;
• (4) detecting second lab data;
• (5) training or retraining said neural network, when said second lab data is detected by step (4), by using a second training set based on said second lab data, and by using a second training set based on second lab data, and by using said first training set;
• (6) detecting third lab data;
• and
• (7) training or retraining said neural network, when said third lab data is detected in step (6), by using a third training set based on said second lab data, and by using a third training set based on third lab data, and using said second training set, without using said first training set;
• wherein at least one of steps (3), (5), and (7) comprises the steps of,
• (a) retrieving, from an historical database, training input data having a timestamp(s);
• (b) selecting a training input data time based on said timestamp(s);
• and
• (c) retrieving an input data indicated by said training input data time.