Dynamic Tikhonov State Forecasting Based on Large-Scale Deep Neural Network Constraints
This work presents dynamic Tikhonov state forecasting based on large-scale deep neural network constraint for the solution to a dynamic inverse problem of electroencephalographic brain mapping. The dynamic constraint is obtained by using a large-scale deep neural network to approximate the dynamics...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-06-01
|
| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/39/1/28 |
| _version_ | 1797580308650917888 |
|---|---|
| author | Cristhian Molina Juan Martinez Eduardo Giraldo |
| author_facet | Cristhian Molina Juan Martinez Eduardo Giraldo |
| author_sort | Cristhian Molina |
| collection | DOAJ |
| description | This work presents dynamic Tikhonov state forecasting based on large-scale deep neural network constraint for the solution to a dynamic inverse problem of electroencephalographic brain mapping. The dynamic constraint is obtained by using a large-scale deep neural network to approximate the dynamics of the state evolution in a discrete large-scale state-space model. An evaluation by using neural networks with several hidden layer configurations is performed to obtain the adequate structure for large-scale system dynamic tracking. The proposed approach is evaluated over two models of 2004 and 10,016 states in discrete time. The models are related to an electroencephalographic problem for EEG generation. A comparison analysis is performed by using static and dynamic Tikhonov approaches with simplified dynamic constraints. By considering the obtained results it can be concluded that the deep neural networks adequately approximate large-scale state dynamics by improving the dynamic inverse problem solutions. |
| first_indexed | 2024-03-10T22:48:17Z |
| format | Article |
| id | doaj.art-63f40c1ed9014fa2a8d840db512bcd2f |
| institution | Directory Open Access Journal |
| issn | 2673-4591 |
| language | English |
| last_indexed | 2024-03-10T22:48:17Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj.art-63f40c1ed9014fa2a8d840db512bcd2f2023-11-19T10:30:36ZengMDPI AGEngineering Proceedings2673-45912023-06-013912810.3390/engproc2023039028Dynamic Tikhonov State Forecasting Based on Large-Scale Deep Neural Network ConstraintsCristhian Molina0Juan Martinez1Eduardo Giraldo2Department of Systems, Instituto Tecnológico Metropolitano, Medellín 050012, ColombiaSchool Applied Sciences and Engineering, Universidad EAFIT, Medellín 050021, ColombiaDepartment of Electrical Engineering, Research Group in Automatic Control, Universidad Tecnológica de Pereira, Pereira 660003, ColombiaThis work presents dynamic Tikhonov state forecasting based on large-scale deep neural network constraint for the solution to a dynamic inverse problem of electroencephalographic brain mapping. The dynamic constraint is obtained by using a large-scale deep neural network to approximate the dynamics of the state evolution in a discrete large-scale state-space model. An evaluation by using neural networks with several hidden layer configurations is performed to obtain the adequate structure for large-scale system dynamic tracking. The proposed approach is evaluated over two models of 2004 and 10,016 states in discrete time. The models are related to an electroencephalographic problem for EEG generation. A comparison analysis is performed by using static and dynamic Tikhonov approaches with simplified dynamic constraints. By considering the obtained results it can be concluded that the deep neural networks adequately approximate large-scale state dynamics by improving the dynamic inverse problem solutions.https://www.mdpi.com/2673-4591/39/1/28dynamic state forecastingdeep neural networklarge scale |
| spellingShingle | Cristhian Molina Juan Martinez Eduardo Giraldo Dynamic Tikhonov State Forecasting Based on Large-Scale Deep Neural Network Constraints Engineering Proceedings dynamic state forecasting deep neural network large scale |
| title | Dynamic Tikhonov State Forecasting Based on Large-Scale Deep Neural Network Constraints |
| title_full | Dynamic Tikhonov State Forecasting Based on Large-Scale Deep Neural Network Constraints |
| title_fullStr | Dynamic Tikhonov State Forecasting Based on Large-Scale Deep Neural Network Constraints |
| title_full_unstemmed | Dynamic Tikhonov State Forecasting Based on Large-Scale Deep Neural Network Constraints |
| title_short | Dynamic Tikhonov State Forecasting Based on Large-Scale Deep Neural Network Constraints |
| title_sort | dynamic tikhonov state forecasting based on large scale deep neural network constraints |
| topic | dynamic state forecasting deep neural network large scale |
| url | https://www.mdpi.com/2673-4591/39/1/28 |
| work_keys_str_mv | AT cristhianmolina dynamictikhonovstateforecastingbasedonlargescaledeepneuralnetworkconstraints AT juanmartinez dynamictikhonovstateforecastingbasedonlargescaledeepneuralnetworkconstraints AT eduardogiraldo dynamictikhonovstateforecastingbasedonlargescaledeepneuralnetworkconstraints |