A linear approximation for training Recurrent Random Neural Networks

Date

1998-01-01

Author

Halıcı, Uğur

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In this paper, a linear approximation for Gelenbe's Learning Algorithm developed for training Recurrent Random Neural Networks (RRNN) is proposed. Gelenbe's learning algorithm uses gradient descent of a quadratic error function in which the main computational effort is for obtaining the inverse of an n-by-n matrix. In this paper, the inverse of this matrix is approximated with a linear term and the efficiency of the approximated algorithm is examined when RRNN is trained as autoassociative memory.

URI

https://hdl.handle.net/11511/52868

Conference Name

13th International Symposium on Computer and Information Sciences (ISCIS 98)

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

U. Halıcı, “A linear approximation for training Recurrent Random Neural Networks,” 1998, vol. 53, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/52868.