Please use this identifier to cite or link to this item:
Title: Growing cascade correlation networks in two dimensions: A heuristic approach
Authors: Su, L.
Guan, S.U. 
Yeo, Y.C. 
Keywords: Cascade correlation neural network
Fan-in number
Heuristic P-T
Propagation delay
Pyramid-tower architecture
Issue Date: 2001
Citation: Su, L.,Guan, S.U.,Yeo, Y.C. (2001). Growing cascade correlation networks in two dimensions: A heuristic approach. Journal of Intelligent Systems 11 (4) : 249-267. ScholarBank@NUS Repository.
Abstract: Dynamic neural network algorithms are used for automatic network design to avoid a time-consuming search for finding an appropriate network topology with trial-and-error methods. Cascade Correlation Network (CCN) is one of the constructive methods to build network architecture automatically. CCN faces problems such as large propagation delays and high fan-in. We present a Heuristic Pyramid-Tower (HPT) neural network designed to overcome the shortcomings of CCN. Benchmarking results for the three real-world problems are reported. The simulation results show that a smaller network depth and reduced fan-in can be achieved using HPT as compared with the original CCN.
Source Title: Journal of Intelligent Systems
ISSN: 03341860
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

checked on Aug 18, 2019

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.