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
In this paper we show how a combination of multiple neuromorphic vision sensors can achieve the same higher level visual processing tasks as carried out by a conventional vision system. We process the multiple neuromorphic sensory signals with a standard auto-regression method in order to fuse the sensory signals and to achieve higher level vision processing tasks at a very high update rate. We also argue why this result is of great relevance for the application domain of reactive and lightweight mobile robotics, at the hands of a soccer robot, where the fastest sensory-motor feedback loop is imperative for a successful participation in a RoboCup soccer competition.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.