A Resource-Allocating Network for Function Interpolation.

Abstract:

:We have created a network that allocates a new computational unit whenever an unusual pattern is presented to the network. This network forms compact representations, yet learns easily and rapidly. The network can be used at any time in the learning process and the learning patterns do not have to be repeated. The units in this network respond to only a local region of the space of input values. The network learns by allocating new units and adjusting the parameters of existing units. If the network performs poorly on a presented pattern, then a new unit is allocated that corrects the response to the presented pattern. If the network performs well on a presented pattern, then the network parameters are updated using standard LMS gradient descent. We have obtained good results with our resource-allocating network (RAN). For predicting the Mackey-Glass chaotic time series, RAN learns much faster than do those using backpropagation networks and uses a comparable number of synapses.

journal_name

Neural Comput

journal_title

Neural computation

authors

Platt J

doi

10.1162/neco.1991.3.2.213

subject

Has Abstract

pub_date

1991-07-01 00:00:00

pages

213-225

issue

2

eissn

0899-7667

issn

1530-888X

journal_volume

3

pub_type

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