@inproceedings{7203dae524fc405a823efaaca25db653,
title = "A VLSI-based Gaussian kernel mapper for real-time RBF neural networks",
abstract = "An analog VLSI circuit approach to a radial basis function (RBF) neural network is explored. For each of a number of reference pattern templates, the circuit calculates the Euclidean distance between that template and an unknown point, and maps each distance to a point on the Gaussian surface of that template. Then, these points may either be added in order to form the basis for an RBF approximator or laterally inhibited to form the basis for an RBF classifier. The circuitry for this network has been implemented in 2-micron CMOS technology, and will form the bases for truly parallel and simultaneous standalone neural networks that function in real time without intervention from conventional computers.",
author = "S. Wolpert and Osborn, {M. J.} and Musavi, {M. T.}",
note = "Publisher Copyright: {\textcopyright} 1992 IEEE.; 18th IEEE Annual Northeast Bioengineering Conference, NEBEC 1992 ; Conference date: 12-03-1992 Through 13-03-1992",
year = "1992",
doi = "10.1109/NEBC.1992.285918",
language = "English (US)",
series = "Proceedings of the IEEE Annual Northeast Bioengineering Conference, NEBEC",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "51--52",
booktitle = "Proceedings of the 18th IEEE Annual Northeast Bioengineering Conference, NEBEC 1992",
address = "United States",
}