Method for steady-state identification based upon identified dynamics

US6047221A · kind A · utility

Assignee

• Pavilion Technologies, Inc. · US

Inventors

• Stephen Piche · Austin, US
• James D. Keeler · Austin, US
• Eric Hartman · Austin, US
• William Douglas Johnson · Austin, US
• Mark Gerules · Cedar Park, US
• Kadir Liano · Pflugerville, US

Key dates

• Filing date: Oct 3, 1997
• Grant date: Apr 4, 2000
• Expiry date: Oct 3, 2017

Classification

• Technology area (CPC G): Physics
• CPC primary: G05B13/048
• WIPO field: Control
• WIPO sector: Instruments

Abstract

A method for modeling a steady-state network in the absence of steady-state historical data. A steady-state neural network can be tied by impressing the dynamics of the system onto the input data during the training operation by first determining the dynamics in a local region of the input space, this providing a set of dynamic training data. This dynamic training data is then utilized to train a dynamic model, gain thereof then set equal to unity such that the dynamic model is now valid over the entire input space. This is a linear model, and the historical data over the entire input space is then processed through this model prior to input to the neural network during training thereof to remove the dynamic component from the data, leaving the steady-state component for the purpose of training. This provides a valid model in the presence of historical data that has a large content of dynamic behavior. A single dynamic model is required for each output variable in a multi-input multi-output steady-state model such that for each output there is a separate dynamic model required for pre-filtering. They are combined in a single network made up of multiple individual steady-state model…