Optimal control of dynamic systems via linearizable deep learning

Patent metadata

Field	Value
Publication number	US-2023251608-A1
Application number	US-202217665944-A
Country	US
Kind code	A1
Filing date	Feb 7, 2022
Priority date	Feb 7, 2022
Publication date	Aug 10, 2023
Grant date	—

Abstract

A method includes: receiving, by a computing device, data from sensors in a manufacturing environment; mapping, by the computing device, the data into a deep learning network; learning, by the computing device, correlations between inputs and outputs of the manufacturing environment using the data; pruning, by the computing device, the deep learning network; predicting, by the computing device and using the pruned network, an output of the pruned network from the inputs of the manufacturing environment; linearizing, by the computing device, the pruned network; optimizing, by the computing device, the output of the linearized pruned network to calculate predicted inputs for the manufacturing environment; and changing, by the computing device, operation inputs in the manufacturing environment to match the predicted inputs.

First claim

What is claimed is:

1 . A method, comprising:
receiving, by a computing device, data from sensors in a manufacturing environment; mapping, by the computing device, the data into a deep learning network; learning, by the computing device, correlations between inputs and outputs of the manufacturing environment using the data; pruning, by the computing device, the deep learning network; predicting, by the computing device and using the pruned network, an output of the pruned network from the inputs of the manufacturing environment; linearizing, by the computing device, the pruned network; optimizing, by the computing device, the output of the linearized pruned network to calculate predicted inputs for the manufacturing environment; and changing, by the computing device, operation inputs in the manufacturing environment to match the predicted inputs.

2 . The method of claim 1 , wherein the sensors are based on supervisory control and data acquisition (SCADA) architecture.

3 . The method of claim 1 , wherein the sensors are based on data acquisition (DAQ) architecture.

4 . The method of claim 1 , wherein the deep learning network is a recurrent neural network (RNN) network.

5 . The method of claim 4 , wherein the RNN network is a long-short term memory (LSTM) network.

6 . The method of claim 1 , further comprising linearizing the deep learning network by replacing a rectified linear unit (ReLU) activation function with a set of equivalent linear equations to the deep learning network in response to the deep learning network being a RNN network.

7 . The method of claim 1 , further comprising:
linearizing the deep learning network by replacing a tanh activation function with a piecewise linear function (PLU) activation function; and reformulating the PLU activation function into a set of equivalent linear equations to the deep learning network in response to the deep learning network being a LSTM network.

8 . The method of claim 1 , further comprising linearizing the deep learning network by replacing a bilinear term in the deep learning network by the McCormick envelope.

9 . The method of claim 1 , wherein the pruning the deep learning network includes removing redundant neurons in the deep learning network.

10 . The method of claim 9 , wherein the pruning the deep learning network includes removing redundant connections of the redundant neurons.

11 . The method of claim 1 , wherein the manufacturing environment is a dynamic manufacturing environment.

12 . The method of claim 1 , wherein the computing device includes software provided as a service in a cloud environment.

13 . A computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to:
receive data from sensors in a manufacturing environment; map the data into a deep learning network; learn correlations between inputs and outputs of the manufacturing environment using the data; prune the deep learning network; predict inputs for the manufacturing environment using the pruned deep learning network; linearize the pruned deep learning network; optimize a predicted output from the linearized pruned deep learning network to calculate predicted inputs for the manufacturing environment; and change operation inputs in the dynamic manufacturing environment to match the predicted inputs.

14 . The computer program product of claim 13 , wherein the deep learning network is a recurrent neural network (RNN) network.

15 . The computer program product of claim 14 , wherein the RNN network is a long-short term memory (LSTM) network.

16 . The computer program product of claim 13 , wherein the sensors are based on supervisory control and data acquisition (SCADA) architecture.

17 . A system comprising:
a processor, a computer readable memory, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to: receive data from sensors in a dynamic manufacturing environment; map the data into a recurrent neural network (RNN) network; learn correlations between inputs and outputs of the dynamic manufacturing environment using the RNN network; prune the RNN network; predict inputs for the dynamic manufacturing environment using the pruned RNN network; linearize the pruned RNN network; optimize a predicted output from the linearized pruned RNN network to calculate predicted inputs; and change operation inputs in the dynamic manufacturing environment to match the predicted inputs.

18 . The system of claim 17 , wherein the RNN network is a long-short term memory (LSTM) network.

19 . The system of claim 17 , wherein the pruning the RNN network includes removing redundant neurons and connections in the RNN network.

20 . The system of claim 17 , wherein the changing the operation inputs includes inputting the predicted inputs into components of the dynamic manufacturing environment.

Assignees

• IBM

Inventors

• Phan Dzung Tien
• Kalagnanam Jayant R
• Nguyen Lam Minh

Classifications

• G06N3/0442

  characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] · CPC title

• G06N3/082

  modifying the architecture, e.g. adding, deleting or silencing nodes or connections · CPC title

• G06N3/048

  Activation functions · CPC title

• G06N3/09

  Supervised learning · CPC title

• G05B13/027 Primary

  using neural networks only · CPC title