Parametric universal nonlinear dynamics approximator and use

Abstract

System and method for modeling a nonlinear process. A combined model for predictive optimization or control of a nonlinear process includes a nonlinear approximator, coupled to a parameterized dynamic or static model, operable to model the nonlinear process. The nonlinear approximator receives process inputs, and generates parameters for the parameterized dynamic model. The parameterized dynamic model receives the parameters and process inputs, and generates predicted process outputs based on the parameters and process inputs, where the predicted process outputs are useable to analyze and/or control the nonlinear process. The combined model may be trained in an integrated manner, e.g., substantially concurrently, by identifying process inputs and outputs (I/O), collecting data for process I/O, determining constraints on model behavior from prior knowledge, formulating an optimization problem, executing an optimization algorithm to determine model parameters subject to the determined constraints, and verifying the compliance of the model with the constraints.

Description

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AI Technical Summary

Benefits of technology

This patent describes an approach called PUNDA which helps create a better understanding of how a certain process works by combining data from experiments or other sources like physics principles. This can help improve efficiency when optimizing and controlling the process in real-time. It also enables us to approximate the behavior of nonlinear processes more accurately than before.

Problems solved by technology

This patent discusses different ways to combine neural network models with traditional mathematical models to improve the performance of these models in various processes. However, there are limitations in current practices, particularly related to uncertainties in the underlying mechanisms and incomplete description of the process. The technical problem being solved is how to effectively incorporate advanced learning algorithms into combined models without requiring extensive re-training of the models or sacrificing accuracy in representing important aspects of the process.

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