NDLI: Analysis of associative reinforcement learning in neural networks using iterated function systems

Content Provider	IEEE Xplore Digital Library
Author	Bressloff, P.C. Stark, J.
Copyright Year	1971
Abstract	A mathematical theory of associative reinforcement learning in neural networks is developed in terms of random iterated function systems (IFSs), which are finite sets of random maps on metric spaces. In particular, the stochastic search for an associative mapping that maximizes the expected pay-off arising from reinforcement is formulated as a random IFS on weight-space. The dynamical evolution of the weights is described by a Markov process. If this process is ergodic then the limiting behavior of the system is described by an invariant probability measure on weight space that can have a fractal-like structure. A class of associative reinforcement learning algorithms is constructed that is an extension of the nonassociative schemes used in stochastic automata theory. The issue of generalization is discussed within the IFS framework and related to the stochastic and possibly fractal nature of the learning process.<>
Starting Page	1348
Ending Page	1360
Page Count	13
File Size	1308559
File Format	PDF
ISSN	00189472
Volume Number	22
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1992-11-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Intelligent networks Neural networks Learning automata Stochastic processes Extraterrestrial measurements Fractals Markov processes Weight measurement Supervised learning Feedback
Content Type	Text
Resource Type	Article
Subject	Engineering

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