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.
Original language | English |
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Pages (from-to) | 249-267 |
Number of pages | 19 |
Journal | Journal of Intelligent Systems |
Volume | 11 |
Issue number | 4 |
DOIs | |
Publication status | Published - 2001 |
Externally published | Yes |
Keywords
- Cascade correlation neural network
- Fan-in number
- Heuristic P-T
- Propagation delay
- Pyramid-tower architecture