The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Some Properties on the Capability of Associative Memory for Higher Order Neural Networks
|
Author(s): Hiromi Miyajima (Kagoshima University, Japan), Shuji Yatsuki (Yatsuki Information System, Inc., Japan), Noritaka Shigei (Kagoshima University, Japan)and Hirofumi Miyajima (Kagoshima University, Japan)
Copyright: 2017
Pages: 30
Source title:
Nature-Inspired Computing: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-0788-8.ch004
Purchase
|
Abstract
Higher order neural networks (HONNs) have been proposed as new systems. In this paper, we show some theoretical results of associative capability of HONNs. As one of them, memory capacity of HONNs is much larger than one of the conventional neural networks. Further, we show some theoretical results on homogeneous higher order neural networks (HHONNs), in which each neuron has identical weights. HHONNs can realize shift-invariant associative memory, that is, HHONNs can associate not only a memorized pattern but also its shifted ones.
Related Content
P. Chitra, A. Saleem Raja, V. Sivakumar.
© 2024.
24 pages.
|
K. Ezhilarasan, K. Somasundaram, T. Kalaiselvi, Praveenkumar Somasundaram, S. Karthigai Selvi, A. Jeevarekha.
© 2024.
36 pages.
|
Kande Archana, V. Kamakshi Prasad, M. Ashok.
© 2024.
17 pages.
|
Ritesh Kumar Jain, Kamal Kant Hiran.
© 2024.
23 pages.
|
U. Vignesh, R. Elakya.
© 2024.
13 pages.
|
S. Karthigai Selvi, R. Siva Shankar, K. Ezhilarasan.
© 2024.
16 pages.
|
Vemasani Varshini, Maheswari Raja, Sharath Kumar Jagannathan.
© 2024.
20 pages.
|
|
|