| Author | Meegama, Ravinda Gayan Narendra |
| Call Number | AIT Thesis no. CS-99-13 |
| Subject(s) | Neural networks (Computer science)
|
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of
Science, School of Engineering and Technology |
| Publisher | Asian Institute of Technology |
| Abstract | A neural network is an efficient tool to forecast and filter a time series which exhibits
non-linearity and consists of trends. In this study, two neural network models are
implemented with Backpropagation algorithm and B-spline basis functions. The results of
forecasting and filtering are compared by analyzing the efficiency indices of calibrating
and validating these two network models. Two data sets, one with water discharge of two
rivers and the other with economic data collected over ten years, have been used for this
analysis. During the analysis, Backpropagation models proved to be more efficient than
B-spline networks and trend removal of river flow data yielded reduced inefficiencies. |
| Year | 1999 |
| Type | Thesis |
| School | School of Engineering and Technology (SET) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Computer Science (CS) |
| Chairperson(s) | Huynh Ngoc Phien |
| Examination Committee(s) | Kanchana Kanchanasut;Hoang Le Tien |
| Scholarship Donor(s) | Royal Thai Government |
| Degree | Thesis (M.Sc.) - Asian Institute of Technology, 1999 |