NDLI: Two architectures for implementing RBF neural networks

Content Provider	IEEE Xplore Digital Library
Author	Wang, C.-J. Hirose, R.T.
Copyright Year	1994
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Colorado Univ., Colorado Springs, CO, USA (Wang, C.-J.; Hirose, R.T.)
Abstract	This paper describes two methods for implementing radial basis function (RBF) neural networks using CMOS. The first method (i.e., the parallel method) is designed to maximally exploit the parallelism of neural computing. The distance between an input vector and each of the stored prototypes is calculated at the same time in the parallel method. The concept of an input plane and an output plane is introduced in the parallel method. The second method (i.e., the serial method) is designed to minimize the chip area required to implement the RBF network. The distance between an input vector and each of the stored prototypes is calculated sequentially in the serial method. Two buses (i.e., the weight bus and the threshold-value bus) are introduced in the serial method. All circuits used in the serial and parallel methods are presented and simulated using SPICE. The entire network is verified by SPICE using two examples. Performance comparison of using the serial and parallel methods is also presented.
Starting Page	1963
Ending Page	1968
File Size	541959
Page Count	6
File Format	PDF
ISBN	078031901X
DOI	10.1109/ICNN.1994.374462
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-06-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Radial basis function networks Biological neural networks Prototypes SPICE Backpropagation algorithms Springs Design methodology Circuit simulation Feedforward neural networks
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

From multilayer perceptrons to radial basis function networks: a comparative study

RBF Neural Networks and Its Application in the Simulation of a Random Vibration System

Modeling transformer internal short circuit faults using neural network techniques

A novel reformulated radial basis function neural network

Growing radial basis neural networks

Application of MLP and RBF networks to cloud detection

Face Recognition Based on Radial Basis Function Neural Networks using Subtractive Clustering Algorithm

RBF Neural Network Application to Face Recognition

An evolutionary clustering technique with local search to design RBF neural network classifiers

Two architectures for implementing RBF neural networks

Similar Documents

From multilayer perceptrons to radial basis function networks: a comparative study

RBF Neural Networks and Its Application in the Simulation of a Random Vibration System

Modeling transformer internal short circuit faults using neural network techniques

A novel reformulated radial basis function neural network

Growing radial basis neural networks

Application of MLP and RBF networks to cloud detection

Face Recognition Based on Radial Basis Function Neural Networks using Subtractive Clustering Algorithm

RBF Neural Network Application to Face Recognition

An evolutionary clustering technique with local search to design RBF neural network classifiers

Two architectures for implementing RBF neural networks