SOME TOPICS IN INDUSTRIAL AND APPLIED MATHEMATICS by edited by Rolf Jeltsch, Ta-Tsien Li, & Ian H Sloan

By edited by Rolf Jeltsch, Ta-Tsien Li, & Ian H Sloan

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Reasonably fast learning has been demonstrated for CSRNs in performing computational tasks, like learning to navigate arbitrary mazes from sight and like evaluating the connectivity of an image [IKW08,YC99]. MLPs simply cannot perform these tasks. Simple feedforward implementations of Object Nets have generated improvements in Wide-Area Control for electric power [QVH07] and in playing chess. Using a feedforward Object Net as a “critic” or “position evaluator,” Fogel’s team [Fogel04] developed the world’s first computer system, which learned to play master-class chess without having been told specific rules of play by human experts.

References [AS95] CG Atkeson and S Schaal, Memory-based neural networks for robot learning, Neurocomputing, 9(3), 243–269, December 1995. [Barron93] AR Barron, Universal approximation bounds for superpositions of a sigmoidal function, IEEE Transactions on Information Theory, 39(3), 930–945, 1993. [BDJ08] SN Balakrishnan, J Ding, and FL Lewis, Issues on stability of ADP feedback controllers for dynamical systems, IEEE Transactions on Systems, Man, and Cybernetics, 38(4), 913–917, August 2008. edu/acs/, on the web site of the Automation and Robotics Research Institute of the University of Texas at Arlington.

MSW90] WT Miller, R Sutton, and P Werbos (eds), Neural Networks for Control, MIT Press, Cambridge, MA, 1990, now in paper. [NSF07] National Science Foundation, Cognitive optimization and prediction: From neural systems to neurotechnology, Emerging Frontiers in Research Initiation 2008 (EFRI-2008), NSF document NSF 07-579. pdf [PEL00] JC Principe, NR Euliano, and W Curt Lefebvre, Neural and Adaptive Systems: Fundamentals through Simulations, Wiley, New York, 2000. [PJX00] JC Principe, JW Fisher, and D Xu, Information theoretic learning, in Unsupervised Adaptive Filtering (ed.

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