1

Stephen Piche, James David Keeler, Eric Hartman, William D Johnson, Mark Gerules, Kadir Liano:
Method for steady-state identification based upon identified dynamics.
Pavilion Technologies,
Gregory M Howison,
April 4, 2000:
US06047221
(131 worldwide citation)

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, th ...

2

James D Keeler, Eric J Hartman, Kadir Liano, Ralph B Ferguson:
Residual activation neural network.
Pavilion Technologies,
Ross Howison Clapp & Korn,
October 4, 1994:
US05353207
(114 worldwide citation)

A plant (72) is operable to receive control inputs c(t) and provide an output y(t). The plant (72) has associated therewith state variables s(t) that are not variable. A control network (74) is provided that accurately models the plant (72). The output of the control network (74) provides a predicte ...

3

James D Keeler, Eric J Hartman, Kadir Liano, Ralph B Ferguson:
Residual activation neural network.
Pavilion Technologies,
Gregory M Howison,
September 24, 1996:
US05559690
(87 worldwide citation)

A plant (72) is operable to receive control inputs c(t) and provide an output y(t). The plant (72) has associated therewith state variables s(t) that are not variable. A control network (74) is provided that accurately models the plant (72). The output of the control network (74) provides a predicte ...

4

James David Keeler, Eric Jon Hartman, Kadir Liano, Ralph Bruce Ferguson:
Residual activation neural network.
Pavilion Technologies,
Gregory M Howison,
January 12, 1999:
US05859773
(73 worldwide citation)

A plant (72) is operable to receive control inputs c(t) and provide an output y(t). The plant (72) has associated therewith state variables s(t) that are not variable. A control network (74) is provided that accurately models the plant (72). The output of the control network (74) provides a predicte ...

5

James David Keeler, Eric J Hartman, Kadir Liano:
Method and apparatus for determining the sensitivity of inputs to a neural network on output parameters.
Pavilion Technologies,
Gregory M Howison,
October 20, 1998:
US05825646
(30 worldwide citation)

A distributed control system (14) receives on the input thereof the control inputs and then outputs control signals to a plant (10) for the operation thereof. The measured variables of the plant and the control inputs are input to a predictive model (34) that operates in conjunction with an inverse ...

6

Bijan Sayyarrodsari, Eric Hartman, Celso Axelrud, Kadir Liano:
System and method of applying adaptive control to the control of particle accelerators with varying dynamics behavioral characteristics using a nonlinear model predictive control technology.
Pavilion Technologies,
Meyertons Hood Kivlin Kowert & Goetzel P C, Jeffrey C Hood,
February 27, 2007:
US07184845
(22 worldwide citation)

The present invention provides a method for controlling nonlinear control problems within particle accelerators. This method involves first utilizing software tools to identify variable inputs and controlled variables associated with the particle accelerator, wherein at least one variable input para ...

7

Bijan Sayyarrodsari, Eric Hartman, Celso Axelrud, Kadir Liano:
Controlling a non-linear process with varying dynamics using non-linear model predictive control.
Rockwell Automation Technologies,
Fletcher Yoder PC, Alexander R Kuszewski,
October 6, 2009:
US07599749
(19 worldwide citation)

The present invention provides a method for controlling nonlinear control problems within particle accelerators. This method involves first utilizing software tools to identify variable inputs and controlled variables associated with the particle accelerator, wherein at least one variable input para ...

8

9

Bijan Sayyar Rodsari, Edward Plumer, Eric Hartman, Kadir Liano, Celso Axelrud:
Training a model of a non-linear process.
Rockwell Automation Technologies,
Fletcher Yoder, William R Walbrun, John M Miller,
September 13, 2011:
US08019701
(16 worldwide citation)

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 proce ...

10