Abslrocf-EchoState Networks (ESNs) use a recurrent artificial damping condition on the largest eigenvalue X,of the neural network as a reservoir. Finding a good one depends on related ad;acency, or connectivi~ matrix. ~1~ the output choosing the right parameters for the genemtian Ofthe reservoir, can be fed back to the reservoir. This leads to a fast intuition and luck. The method proposed in this article eliminates training procedure of the output weights and the the need for the tuning by hand by replacing it with a double pprameters which genemte , , , e reservoir is used, Then a search directly on the ronnecti\ity matrices fie-hmes the ESN. Both steps show improvements over other h o r n methods for an experimental limit-cycle dataset of the Twin-Burger underwater robot e,,alutiomry computation, First a broad seprrh to h d the right
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