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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/1500

Title: Growing Cascade Correlation Networks in Two Dimensions - A Heuristic Approach
Authors: Li, S
Guan, SU
Yeo, YC
Keywords: Cascade Correlation Neural Network, Pyramid-Tower Architecture, Heuristic P-T, Propagation Delay, Fan-in Number
Publication Date: 2001
Publisher: Freund & Pettman
Citation: Li Su, Sheng-Uei Guan and Ye-Chuan Yeo, “Growing Cascade Correlation Networks in Two Dimensions -- A Heuristic Approach”, Journal of Intelligent Systems, 249-268, Vol. 11, No. 4, 2001.
Abstract: Dynamic neural network algorithms are used for automatic network design in order to avoid 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. In this paper, 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 to the original CCN.
URI: http://bura.brunel.ac.uk/handle/2438/1500
ISSN: 0334-1860
Appears in Collections:School of Engineering and Design Research papers
Electronic and Computer Engineering

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