Includes bibliographical references.
|Statement||Jor̈g Kindermann, Alexander Linden (Hrsg.).|
|Series||GMD-Bericht ; Nr. 185, Berichte der Gesellschaft für Mathematik und Datenverarbeitung -- Nr. 185.|
|Contributions||Kindermann, Jörg., Linden, Alexander., Workshop on Distributed Adaptive Neural Information Processing. (1989 : Schloss Birlinghoven)|
|LC Classifications||QA76.87 .D570 1989|
|The Physical Object|
|Pagination||254 p. :|
|Number of Pages||254|
Get this from a library! Distributed adaptive neural information processing: proceedings of a workshop at Schloss Birlinghoven, - April [Jörg Kindermann; Workshop on Distributed Adaptive Neural Information Processing (, Sankt Augustin);]. CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We develop a protocol for optimizing dynamic behavior of a network of simple electronic components, such as a sensor network, an ad hoc network of mobile devices, or a network of communication switches. This protocol requires only local communication and simple computations which are distributed among devices. Interactive Neural Network Book. The interactive book "Neural and Adaptive Systems: Fundamentals Through Simulations (ISBN: )" by Principe, Euliano, and Lefebvre, has been published by John Wiley and Sons and is available for purchase directly through enthusiasm for this book is best expressed by the response of our readers. By employing an adaptive neural networks state observer to approximate the unknown nonlinear functions and to reconstruct the immeasurable inner states, we propose a novel distributed output feedback formation control protocol with the backstepping method combining with the dynamic surface control technique.
This book is an outgrowth of the workshop on Neural Adaptive Control Technology, NACT I, held in in Glasgow. Information Processing and Routing in Wireless Sensor Networks. Security in Distributed and Networking Systems. Encyclopedia on Ad Hoc and Ubiquitous Computing. Neural Information Processing Systems (NeurIPS ). Information-theoretic lower bounds for distributed statistical estimation with communication constraints, Yuchen Zhang, John C. Duchi, Michael I. Jordan, and Martin Wainwright. Neural Information Processing Systems (NeurIPS ). Gao S, Dong H and Ning B () Observer-based nonlinear feedback decentralized neural adaptive dynamic surface control for large-scale nonlinear systems, International Journal of Adaptive Control and Signal Processing, , (), Online publication date: 1-Nov In Advances in Neural Information Processing Systems. Google Scholar; Shai Shalev-Shwartz. Online learning and online convex optimization. Foundations and Trends in Machine Learning, Google Scholar; Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, and Lin Xiao. Optimal distributed online prediction using mini-batches. J. Mach. Learn.
In this paper, the distributed synchronization of stochastic coupled neural networks with time-varying delay is concerned via randomly occurring control. We use two Bernoulli stochastic variables to describe the occurrence of distributed adaptive control and updating law according to certain probabilities. The distributed adaptive control and updating law for each vertex in the network depend. The six volume set LNCS , LNCS , LNCS , LNCS , LNCS , and LNCS constituts the proceedings of the 24rd International Conference on Neural Information Processing, ICONIP , held in Guangzhou, China, in November The full papers presented were carefully reviewed and selected from submissions. Neural computation belongs to information processing dealing with adaptive, parallel, and distributed (localized) signal processing. In data analysis, a common task consists in finding an adequate subspace of multivariate data for subsequent processing and interpretation. Advances in Neural Information Processing Systems 26 (NIPS ) The papers below appear in Advances in Neural Information Processing Systems 26 edited by C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger. They are proceedings from the conference, "Neural Information Processing Systems ".