| Author | Hashem, M. M. A. |
| Call Number | AIT Thesis no. CS-93-05 |
| Subject(s) | Neural networks (Computer science)
|
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of
Engineering |
| Publisher | Asian Institute of Technology |
| Abstract | In the present work, Adaptive Resonance Theory 1 (ART1) as a clustering algorithm is
analyzed. Under some network modeling assumptions and enhanced template definitions, some
of its emergent but inherent characteristics related to similarity and learning are investigated and
demonstrated to understand more about the output it generates. The memory capacity in bits (each
bit bears an essential feature of a pattern), its upper and lower limits, and the capacity variation
upon learning of input patterns in real time, which Carpenter & Grossberg avoided in their
original work, are derived. A new idea for a similarity metric in novelty detector (orienting-subsystem)
is proposed to extend its operation over arbitrary pattern sequences in the real-time
for one pattern list presentation. The authenticity of the proposed method for fresh and noisy
pattern environments compared to Carpenter & Grossberg method is also illustrated with some
examples. Moreover, the present work on this paradigm points out the ways of extending this
theory to look into stability-plasticity problems in the context of some real world applications. |
| Year | 1993 |
| 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) | Sadananda, Ramakoti; |
| Examination Committee(s) | Takahashi, Kenzo;Yulu, Qi; |
| Scholarship Donor(s) | Government of Finland; |
| Degree | Thesis (M.Eng.) - Asian Institute of Technology, 1993 |