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City Dallas / Stillwater / / Company BP / NEURAL NETWORKS / LAYERED DIGITAL DYNAMIC NETWORKS / / Country United States / / Currency USD / / / Facility Oklahoma State University / / IndustryTerm arbitrary dynamic networks / Static networks / online version / digital dynamic network / real time / feedforward network / layered recurrent networks / recurrent network / real time implementation / online implementation / test network / dynamic backpropagation algorithms / u;l training algorithms / gradient-based training algorithms / real-time recurrent learning algorithm / ordered networks / real-time recurrent learning / gradient algorithms / recurrent networks / parallel distributed information processing structure / layered feedforward network / dynamic network / higher order networks / spatiotemporal networks / gradient algorithm / feedforward networks / backpropagation algorithm / test networks / / Organization Research Department / Oklahoma State University / School of Electrical and Computer Engineering / / Person Orlando De Jesús / / Position general network frameworks / / ProvinceOrState Texas / / Technology BPTT algorithms / u;l training algorithms / RTRL gradient algorithm / RTRL algorithm / 2007 Backpropagation Algorithms / general algorithms / dynamic backpropagation algorithms / BACKPROPAGATION ALGORITHMS / gradient-based training algorithms / BPTT gradient algorithm / neural network / BPTT algorithm / Jacobian algorithms / single-loop / gradient algorithms / training algorithm / Marquardt algorithm / real-time recurrent learning algorithm / simulation / backpropagation algorithm / RTRL algorithms / Digital Object Identifier / gradient algorithm / /
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14 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 18, NO. 1, JANUARY 2007 Backpropagation Algorithms for a Broad Class of Dynamic Networks