Improved Kohonen Feature Map Associative Memory with Area Representation for Sequential Analog Patterns

Authors:
Tomonori Shirotori;Yuko Osana
Affiliations:
Tokyo University of Technology, Tokyo, Japan;Tokyo University of Technology, Tokyo, Japan
Venue:
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Year:
2009

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Abstract

In this paper, we propose an improved Kohonen feature map associative memory with area representation for sequential analog patterns. This model is based on the conventional Kohonen feature map associative memory with area representation for sequential analog patterns. The proposed model 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.