Title :
Chaotic Complex-valued Multidirectional Associative Memory with variable scaling factor - One-to-many association ability -
Author :
Yoshida, Akio ; Osana, Yuko
Author_Institution :
Sch. of Comput. Sci., Tokyo Univ. of Technol., Hachioji, Japan
fDate :
July 31 2011-Aug. 5 2011
Abstract :
In this paper, we propose a Chaotic Complex-valued Multidirectional Associative Memory (CCMAM) with variable scale factor which can realize one-to-many associations of M-tuple multi-valued patterns. The proposed model is based on the Multidirectional Associative Memory, and is composed of complex-valued neurons and chaotic complex-valued neurons. In the proposed model, associations of multivalued patterns are realized by using complex-valued neurons, and one-to-many associations are realized by using chaotic complex-valued neurons. Moreover, in the proposed model, the appropriate parameters of chaotic complex-valued neurons can be determined easily than in the original Chaotic Complex-valued Multidirectional Associative Memory. We carried out a series of computer experiments and confirmed that the proposed model has superior one-to-many association ability than that of the conventional model.
Keywords :
chaos; content-addressable storage; neural nets; M-tuple multivalued patterns; chaotic complex-valued multidirectional associative memory; chaotic complex-valued neurons; complex-valued neurons; information processing; neural networks; one-to-many association ability; variable scaling factor;
Conference_Titel :
Neural Networks (IJCNN), The 2011 International Joint Conference on
Conference_Location :
San Jose, CA
Print_ISBN :
978-1-4244-9635-8
DOI :
10.1109/IJCNN.2011.6033372