Dependence on Memory Pattern in Sensitive Response of Memory Fragments among Three Types of Chaotic Neural Network Models

• Published in: Neural Information Processing. Theory and Algorithms pp. 223 - 230
• Main Authors: Hamada, Toshiyuki, Kuroiwa, Jousuke, Ogura, Hisakazu, Odaka, Tomohiro, Shirai, Haruhiko, Suwa, Izumi
• Format: Book Chapter
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg
• Series: Lecture Notes in Computer Science
• Subjects: CNN; Instability of orbits; memory pattern fragments; memory search
• ISBN: 3642175368; 9783642175367
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-642-17537-4_28

In this paper, we investigate the dependence on the size and the number of memory pattern in the sensitive response to memory pattern fragments in chaotic wandering states among three types of chaotic neural network (CNN) models. From the computer experiments, the three types of chaotic neural network model show that the success ratio is high and the accessing time is short without depending on the size and the number of the memory patterns. The feature is introduced in chaotic wandering states with weaker instability of orbits and stronger randomness in memory pattern space. Thus, chaos in the three model is practical in the memory pattern search.