| Author | Dhargye, Trinley |
| Call Number | AIT Thesis no. CS-99-19 |
| Subject(s) | Neural networks (Computer science)
|
| Note | A thesis submitted in partial fulfillment of the requirements
for the Degree of Master of Engineering, School of Engineering and Technology |
| Publisher | Asian Institute of Technology |
| Abstract | A time series could be considered as a collection of observations made sequentially over time
at almost equal time intervals. In this study, various types of neural networks were developed
using Bernstein polynomials and Ball basis functions to build the most suitable network model
for forecasting and filtering river discharge and some economic data. Two types of Bezier
network models were developed for the said networks. The first model was created by a
combination of univariate Bernstein polynomials and the analytical method, the second model
by taking the tensor product of univariate Bernstein polynomials. The analytical method and
instantaneous learning rule were used to find the weight vector, which keeps the information
of the network under construction.
Applications of these models to several data sets show that they perform well in a number of
cases, regardless of their simple structure. |
| 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) | Finnish Government |
| Degree | Thesis (M.Eng.) - Asian Institute of Technology, 1999 |