Title :
A VLSI-based Gaussian kernel mapper for real-time RBF neural networks
Author :
Wolpert, S. ; Osborn, Michael J. ; Musavi, Mohamad T.
Author_Institution :
Dept. of Electr. Eng., Maine Univ., Orono, ME, USA
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.
Keywords :
CMOS integrated circuits; VLSI; neural nets; 2-micron CMOS technology; Euclidean distance; VLSI-based Gaussian kernel mapper; analog VLSI circuit approach; radial basis function neural network; real-time neural networks; reference pattern templates; standalone neural networks; Artificial neural networks; CMOS technology; Circuits; Computer networks; Differential amplifiers; Euclidean distance; Kernel; Neural networks; Space technology; Very large scale integration;
Conference_Titel :
Bioengineering Conference, 1992., Proceedings of the 1992 Eighteenth IEEE Annual Northeast
Conference_Location :
Kingston, RI, USA
Print_ISBN :
0-7803-0902-2
DOI :
10.1109/NEBC.1992.285918
