Publication Date:
2019-08-28
Description:
An electronic neurocomputer which implements a radial basis function neural network (RBFNN) is described. The RBFNN is a network that utilizes a radial basis function as the transfer function. The key advantages of RBFNNs over existing neural network architectures include reduced learning time and the ease of VLSI implementation. This neurocomputer is based on an analog/digital hybrid design and has been constructed with both custom analog VLSI circuits and a commercially available digital signal processor. The hybrid architecture is selected because it offers high computational performance while compensating for analog inaccuracies, and it features the ability to model large problems.
Keywords:
ELECTRONICS AND ELECTRICAL ENGINEERING
Type:
In: IJCNN - International Joint Conference on Neural Networks, Baltimore, MD, June 7-11, 1992, Proceedings. Vol. 2 (A93-37001 14-63); p. II-607 to II-612.
Format:
text
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