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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Additional resources for SOME TOPICS IN INDUSTRIAL AND APPLIED MATHEMATICS
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.