Title :
Recall and separation ability of chaotic associative memory with variable scaling factor
Author_Institution :
Tokyo Univ. of Technol., Japan
fDate :
6/24/1905 12:00:00 AM
Abstract :
In this paper, we propose a chaotic associative memory (CAM) with variable scaling factor which can separate superimposed patterns and can deal with one-to-many associations. In the proposed model, the appropriate parameters of chaotic neurons can be determined easily than in the original chaotic associative memory
Keywords :
chaos; content-addressable storage; CAM; chaotic associative memory; one-to-many associations; recall ability; separation ability; superimposed pattern separation; variable scaling factor; Associative memory; Biological neural networks; Biological system modeling; CADCAM; Chaos; Computer aided manufacturing; Damping; Integrated circuit modeling; Neural networks; Neurons;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1007575
