제어로봇시스템학회:학술대회논문집
- 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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- Pages.482-482
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- 2000
Generalized Asymmetrical Bidirectional Associative Memory for Human Skill Transfer
- T.D. Eom (Dept. of Electrical Engineering, KAIST) ;
- Lee, J. J. (Dept. of Electrical Engineering, KAIST)
- 발행 : 2000.10.01
초록
The essential requirements of neural network for human skill transfer are fast convergence, high storage capacity, and strong noise immunity. Bidirectional associative memory(BAM) suffering from low storage capacity and abundance of spurious memories is rarely used for skill transfer application though it has fast and wide association characteristics for visual data. This paper suggests generalization of classical BAM structure and new learning algorithm which uses supervised learning to guarantee perfect recall starting with correlation matrix. The generalization is validated to accelerate convergence speed, to increase storage capacity, to lessen spurious memories, to enhance noise immunity, and to enable multiple association using simulation work.
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