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|Title:||Growing cascade correlation networks in two dimensions: A heuristic approach||Authors:||Su, L.
|Keywords:||Cascade correlation neural network
|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||URI:||http://scholarbank.nus.edu.sg/handle/10635/56163||ISSN:||03341860|
|Appears in Collections:||Staff Publications|
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