| Author | Piyachai Leesomprasong |
| Call Number | AIT Thesis no. CS-96-21 |
| Subject(s) | Neural networks (Computer science)
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| Note | A thesis submitted in partial fulfillment of the requirements for degree of Master of Science. |
| Publisher | Asian Institute of Technology |
| Series Statement | Thesis ; no. CS-96-21 |
| Abstract | Artificial Neural Networks (ANNs) have features that can be exploited to solve computational problems. First, the inherently parallel structure of some ANNs makes them suitable for use as parallel computers. Second, the fact that some ANNs can learn input/output mappings and generalize is a feature that has generated a huge amount of interest. ANNs are used to solve certain problem for Interconnection Networks (INs). The subject that is investigated is the ability of RNNs to learn the structure of an IN from examples. The approach is essentially an expansion of the grammatical inference problem that has been explained in the RNN literature. Gradient descent methods are used to train a second order Single Layer Recurrent Neural Network (SLRNN), and the IN structure can then be extracted from the SLRNN using simple clustering algorithms. |
| Year | 1996 |
| Corresponding Series Added Entry | Asian Institute of Technology. Thesis ; no. CS-96-21 |
| 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) | Ramakoti Sadananda |
| Examination Committee(s) | Batanov, D.N.;Yulu, Qi;Shrestha, Amarottam |
| Scholarship Donor(s) | The Communications Authority of Thailand |
| Degree | Thesis (M.Sc.) - Asian Institute of Technology, 1996 |