Neural network process measurement and control

The computer neural network process control method of claim 7, further comprising the steps of:

• sampling the process and generating a product property measurement and a second associated timestamp;
• storing said product property measurement in said historical database with said second associated timestamp;
• and
• training the neural network by adjusting weights of said neural network in accordance with said product property measurement and said at least one process condition measurement.

A computer neural network process control method for controlling a process for producing having at least one product property, comprising the steps of:

• operating the process with one or more sensors connected to sense process conditions and produce at least one process condition measurement for each sensor;
• storing said at least one process condition measurement in an historical database with at least one associated timestamp;
• sampling the process and generating a product property measurement and a second associated timestamp;
• and
• storing said product property measurement in said historical database with said second associated timestamp;
• retrieving said at least one process condition measurement from said historical database for use by said predicting step;
• running a modular neural network process control system, comprising the steps of:
• running a module timing and sequencing means and independently triggering, in accordance with respective module timing specifications, a predicting submodule and a training submodule of a neural network module;
• training a neural network, using said training submodule of said neural network module, when triggering by said module timing and sequencing means, by adjusting weights of said neural network in accordance with said product property measurement and said at least one process condition measurement;
• and
• predicting, using said predicting submodule of said neural network module, first output data using said at least one process condition measurement as input data, when triggered by said module timing and sequencing means;
• controlling an actuator with a supervisory and/or regulatory process controller by computer controller output data using said first output data as controller input data in place of a sensor input data and/or a product property input data;
• and
• changing a controllable process state, using said actuator, in accordance with said controller output data.

A computer neural network process control method for controlling a process for producing a product having at least one product property, comprising the steps of:

• operating the process with one or more sensors connected to sense process conditions and produce at least one process condition measurement for each sensor;
• running a modular neural network process control system, comprising the steps of:
• running a module timing and sequencing means and triggering, in accordance with module timing specifications, a neural network module;
• and
• predicting, using said neural network module, first output data using said at least one process condition measurement as input data, when triggered by said module timing and sequencing means by summing at least two weighted inputs to an element of said neural network;
• controlling an actuator with a supervisory and/or regulatory process controller by computing controller output data using said first output data as controller input data in place of a sensor input data and/or a product property input data;
• and
• changing a controllable process sate, using said actuator, in accordance with said controller output data.

A computer neural network process control method for controlling a process for producing a product having at least one product property, comprising the steps of:

• operating the process with one or more sensors connected to sense process conditions and product at least one process condition measurement for each sensor;
• storing said at least one process condition measurement in an historical database with an associated timestamp;
• sampling the process and generating a product property measurement and a second associated timestamp;
• storing said product property measurement in said historical database with said second associated timestamp;
• retrieving said at least one process condition measurement from said historical database for use by said predicting step.
• running a modular neural network process control system, comprising the steps of:
• running a module timing and sequencing means and independently triggering, in accordance with respective module timing specifications, a predicting submodule and a training submodule of a neural network module and a supervisory and/or regulatory controller module;
• training a neural network, using said training submodule of said neutral network module, when triggered by said module timing and sequencing means, by adjusting weights of said neural network in accordance with said product property measurement and said at least one process condition measurement;
• predicting, using said predicting submodule of said neural network module, first output data using said at least one process condition measurement as input data, when triggered by said module timing and sequencing means;
• and
• controlling an actuator by computing with said supervisory and/or regulatory controller module controller output data using said first output data as controller input data in place of a sensor input data and/or a product property input data, when triggered by said module timing and sequencing means;
• and
• changing a controllable process state, using said actuator, in accordance with said controller output data.

A computer neural network process control system for controlling a process for producing a product having at least one product property, comprising:

• (a) a sensor, for generating a process condition measurement;
• (b) a neural network having predicting means for predicting first output data in accordance with input data;
• (c) connection means for providing said process condition measurement to said predicting means for use as said input data;
• (d) a supervisory and/or regulatory controller for computing a controller output data in accordance with a controller input data, connected to use said first output data as said controller input data in place of a sensor input data and/or a product property input data;
• and
• (e) an actuator, connected to use said controller output data, for changing a controllable process state in accordance with said controller output data.