公开(公告)号：US11693373B2

公开(公告)日：2023-07-04

申请号：US16709775

申请日：2019-12-10

Applicant: California Institute of Technology

Inventor： Guanya Shi ,  Xichen Shi ,  Michael O'Connell ,  Animashree Anandkumar ,  Yisong Yue ,  Soon-Jo Chung

IPC: G05B13/02 ,  G06N3/04 ,  G06N3/084

CPC classification number: G05B13/027 ,  G06N3/04 ,  G06N3/084

Abstract: Systems and methods for learning based control in accordance with embodiments of the invention are illustrated. One embodiment includes a method for training an adaptive controller. The method includes steps for receiving a set of training data that includes several training samples, wherein each training sample includes a state and a true uncertain effect value. The method includes steps for computing an uncertain effect value based on the state, computing a set of one or more losses based on the true uncertain effect value and the computed uncertain effect value, and updating the adaptive controller based on the computed set of losses.

公开(公告)号：US10821285B2

公开(公告)日：2020-11-03

申请号：US16475856

申请日：2018-01-05

Applicant: California Institute of Technology ,  Nicholas A. Terrafranca

Inventor： Joel W. Burdick ,  Yanan Sui ,  Yisong Yue ,  Nicholas A. Terrafranca

IPC: A61N1/36

Abstract: A system, method, and apparatus for identifying optimal or near optimal complex stimulation waveforms for a neurostimulator device or neuromodulation device are disclosed. An example method includes using a dueling bandits algorithm with correlation among stimulation arms to select a batch of stimulation arms for sequential application to a patient during a therapy session. Each of the stimulation arms specifies complex stimulation waveform parameter values. Feedback from applying the stimulation arms to the patient is recorded and used to update feedback reward values corresponding to at least some of the stimulation arms using a stimulation arm correlation index. A second batch of stimulations arms is selected based upon the updated feedback reward values and applied to a patient. The method is iteratively repeated over a number of therapy sessions until an optimal or near optimal batch of stimulation arms (defining complex stimulation waveforms) is determined.

公开(公告)号：US20200183339A1

公开(公告)日：2020-06-11

申请号：US16709775

申请日：2019-12-10

Applicant: California Institute of Technology

Inventor： Guanya Shi ,  Xichen Shi ,  Michael O'Connell ,  Animashree Anandkumar ,  Yisong Yue ,  Soon-Jo Chung

IPC: G05B13/02 ,  G06N3/08 ,  G06N3/04

Abstract: Systems and methods for learning based control in accordance with embodiments of the invention are illustrated. One embodiment includes a method for training an adaptive controller. The method includes steps for receiving a set of training data that includes several training samples, wherein each training sample includes a state and a true uncertain effect value. The method includes steps for computing an uncertain effect value based on the state, computing a set of one or more losses based on the true uncertain effect value and the computed uncertain effect value, and updating the adaptive controller based on the computed set of losses.

公开(公告)号：US20190374777A1

公开(公告)日：2019-12-12

申请号：US16475856

申请日：2018-01-05

Applicant: California Institute of Technology

Inventor： Joel W. Burdick ,  Yanan Sui ,  Yisong Yue ,  Nicholas A. Terrafranca

IPC: A61N1/36

Abstract: A system, method, and apparatus for identifying optimal or near optimal complex stimulation waveforms for a neurostimulator device or neuromodulation device are disclosed. An example method includes using a dueling bandits algorithm with correlation among stimulation arms to select a batch of stimulation arms for sequential application to a patient during a therapy session. Each of the stimulation arms specifies complex stimulation waveform parameter values. Feedback from applying the stimulation arms to the patient is recorded and used to update feedback reward values corresponding to at least some of the stimulation arms using a stimulation arm correlation index. A second batch of stimulations arms is selected based upon the updated feedback reward values and applied to a patient. The method is iteratively repeated over a number of therapy sessions until an optimal or near optimal batch of stimulation arms (defining complex stimulation waveforms) is determined.