IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Some Properties on the Capability of Associative Memory for Higher Order Neural Networks

Some Properties on the Capability of Associative Memory for Higher Order Neural Networks
View Sample PDF
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

View Some Properties on the Capability of Associative Memory for Higher Order Neural Networks on the publisher's website for pricing and purchasing information.

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

Mohamed Arezki Mellal. © 2022. 9 pages.
Tahir Cetin Akinci, Ramazan Caglar, Gokhan Erdemir, Aydin Tarik Zengin, Serhat Seker. © 2022. 11 pages.
Sunanda Hazra, Provas Kumar Roy. © 2022. 16 pages.
Ragab A. El-Sehiemy, Almoataz Y. Abdelaziz. © 2022. 23 pages.
Khaled Dassa, Abdelmadjid Recioui. © 2022. 35 pages.
Anupama Kumari, Mukund Madhaw, C. B. Majumder, Amit Arora. © 2022. 21 pages.
Mandrita Mondal. © 2022. 20 pages.
Body Bottom