Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

January 1993, Vol. 5, No. 1, Pages 105-114
(doi: 10.1162/neco.1993.5.1.105)
© 1993 Massachusetts Institute of Technology
Coarse Coding Resource-Allocating Network
Article PDF (390.64 KB)
Abstract

In recent years localized receptive fields have been the subject of intensive research, due to their learning speed and efficient reconstruction of hypersurfaces. A very efficient implementation for such a network was proposed recently by Platt (1991). This resource-allocating network (RAN) allocates a new neuron whenever an unknown pattern is presented at its input layer. In this paper we introduce a new network architecture and learning paradigm. The aim of our approach is to incorporate "coarse coding" to the resource-allocating network. The network presented here provides for each input coordinate a separate layer, which consists of one-dimensional, locally tuned gaussian neurons. In the following layer multidimensional receptive fields are built by using pi-neurons. Linear neurons aggregate the outputs of the pi-neurons in order to approximate the required input-output mapping. The learning process follows the ideas of the resource-allocating network of Platt but due to the extended architecture of our network other improvements of the learning process had to be defined. Compared to the resource-allocating network a more compact network with comparable accuracy is obtained.