Reconstruction of Nonlinear Systems Using Delay Lines and Feedforward Networks

dc.contributor.authorElliott, David L.en_US
dc.contributor.departmentISRen_US
dc.date.accessioned2007-05-23T09:58:39Z
dc.date.available2007-05-23T09:58:39Z
dc.date.issued1995en_US
dc.description.abstractNonlinear system theory ideas have led to a method for approximating the dynamics of a nonlinear system in a bounded region of its state space, by training a feedforward neural network which is then reconfigured in recursive mode to provide a stand-alone simulator of the system. The input layer of the neural network contains time-delayed samples of one or more system outputs and control inputs. Autonomous systems can be simulated in this way by providing impulse inputs.en_US
dc.format.extent119741 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/5612
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; TR 1995-17en_US
dc.subjectneural systemsen_US
dc.subjectfilteringen_US
dc.subjectnonlinear systemsen_US
dc.subjectIntelligent Signal Processing en_US
dc.subjectCommunications Systemsen_US
dc.titleReconstruction of Nonlinear Systems Using Delay Lines and Feedforward Networksen_US
dc.typeTechnical Reporten_US

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