A Hebbian feedback covariance learning paradigm for self-tuningoptimal control
Young, D.L.
Poon, C.-S.
Div. of Health Sci. & Technol., MIT, Cambridge, MA;
This paper appears in: Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
Publication Date: Apr 2001
Volume: 31,
Issue: 2
On page(s): 173-186
ISSN: 1083-4419
References Cited: 48
CODEN: ITSCFI
INSPEC Accession Number: 6888671
Digital Object Identifier: 10.1109/3477.915341
Current Version Published: 2002-08-07
Abstract
We propose a novel adaptive optimal control paradigm inspired by
Hebbian covariance synaptic adaptation, a preeminent model of learning
and memory as well as other malleable functions in the brain. The
adaptation is driven by the spontaneous fluctuations in the system input
and output, the covariance of which provides useful information about
the changes in the system behavior. The control structure represents a
novel form of associative reinforcement learning in which the
reinforcement signal is implicitly given by the covariance of the
input-output (I/O) signals. Theoretical foundations for the paradigm are
derived using Lyapunov theory and are verified by means of computer
simulations. The learning algorithm is applicable to a general class of
nonlinear adaptive control problems. This on-line direct adaptive
control method benefits from a computationally straightforward design,
proof of convergence, no need for complete system identification,
robustness to noise and uncertainties, and the ability to optimize a
general performance criterion in terms of system states and control
signals. These attractive properties of Hebbian feedback covariance
learning control lend themselves to future investigations into the
computational functions of synaptic plasticity in biological neurons
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