Reservoir Optimization in Recurrent Neural Networks using Kronecker Kernels

In this paper, using the mathematical properties of self-kronecker-production of small size random matrices, a simple but effective method is presented to optimize the reservoir of an echo state network given a certain task. The experimental results investigating the NARMA system show that few steps of the proposed optimization process can lead to a near optimum solution.


Published in:
IEEE ISCAS 2008, 868-871
Presented at:
IEEE ISCAS 2008, Seattle, WA, May 18-21, 2008
Year:
2008
Publisher:
None, IEEE
Other identifiers:
Laboratories:




 Record created 2008-06-16, last modified 2018-03-17


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