Publication Type:

Conference Paper

Source:

Digital Information Management, 2006 1st International Conference on, IEEE, Bangalore (2006)

Other Number:

Page(s): 263 - 268

URL:

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4221900

Abstract:

Pattern classification and function approximation have been found in many applications. The radial basis function network (RBFN) has shown a great promise in this sort of problems because of its faster learning capacity. Though RBFNs have storage properties similar to that ofHopfield networks, these properties have not been well explored so far. In this paper, an approach for analyzing the storage capacity of the RBFN is presented. An upper bound on cost function is found and the error over weighted input vectors is minimized by increasing the number of hidden units. The storage capacity is defined and the proposed method can be used to estimate the capacity in terms of the total probability density function by adding the partial information content associated with each class.

Cite this Research Publication

M. George and Dr. Kaimal, M. R., “On the Storage Capabilities of Radial Basis Function Neural Networks”, in Digital Information Management, 2006 1st International Conference on, Bangalore, 2006.

207
PROGRAMS
OFFERED
5
AMRITA
CAMPUSES
15
CONSTITUENT
SCHOOLS
A
GRADE BY
NAAC, MHRD
9th
RANK(INDIA):
NIRF 2017
150+
INTERNATIONAL
PARTNERS