Time series prediction using real-time recurrent networks
| dc.creator | Li, Ruina | |
| dc.date.accessioned | 2016-11-14T23:09:05Z | |
| dc.date.available | 2011-02-18T23:26:26Z | |
| dc.date.available | 2016-11-14T23:09:05Z | |
| dc.date.issued | 1997-05 | |
| dc.degree.department | Computer Science | en_US |
| dc.description.abstract | The purpose of this work is to investigate the possibility of using time series prediction of the Electrocardiogram (ECG) data by the Real-Time Recurrent Networks (RTRN). The RTRN models have been constructed using the Real-Time Recurrent Learning (RTRL) algorithm with teacher forcing. Both single-point prediction and multi-point prediction were used to forecast the ECG behaviors. The ECG data come from the ECG recordings gathered from a group of patients by the Massachusetts Institute of Technology Division of Health Sciences and Technology. The RTRNs were trained with normal ECG data and were used to predict both normal and abnormal ECG behaviors of the same patient. We found that the single-point prediction of most RTRNs achieved successful results in the forecasting of both normal and abnormal ECG behaviors. However, the multi-point prediction fails to produce the desired results. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/2346/19988 | en_US |
| dc.language.iso | eng | |
| dc.publisher | Texas Tech University | en_US |
| dc.rights.availability | Unrestricted. | |
| dc.subject | Real-time data processing | en_US |
| dc.subject | Machine learning | en_US |
| dc.subject | Neural networks (Computer science) | en_US |
| dc.subject | Electrocardiography | en_US |
| dc.subject | Cardiovascular system -- Diseases | en_US |
| dc.title | Time series prediction using real-time recurrent networks | |
| dc.type | Thesis |