Fast kohonen feature map associative memory using area representation for sequential analog patterns

Authors:
Hiroki Midorikawa;Yuko Osana
Affiliations:
Tokyo University of Technology, Hachioji, Tokyo, Japan;Tokyo University of Technology, Hachioji, Tokyo, Japan
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Year:
2010

Citing 2
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Abstract

In this paper, we propose a Fast Kohonen Feature Map Associative Memory with Area Representation for Sequential Analog Patterns (FKFMAM-AR-SAP). This model is based on the conventional Improved Kohonen Feature Map Associative Memory with Area Representation for Sequential Analog Patterns (IKFMAM-AR-SAP). The proposed model can realize the one-to-many associations even when the first patterns are same in the plural sequential patterns. And, it has enough robustness for noisy input and damaged neurons. Moreover, the learning speed of the proposed model is faster than that of the conventional model. We carried out a series of computer experiments and confirmed the effectiveness of the proposed model.