A new back-propagation neural network optimized with cuckoo search algorithm
Back-propagation Neural Network (BPNN) algorithm is one of the most widely used and a popular technique to optimize the feed forward neural network training. Traditional BP algorithm has some drawbacks, such as getting stuck easily in local minima and slow speed of convergence. Nature inspired meta-...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Conference or Workshop Item |
| Published: |
2013
|
| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/4002/ http://eprints.uthm.edu.my/4002/1/A_New_Back%2DPropagation_Neural_Network.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Back-propagation Neural Network (BPNN) algorithm is one of the
most widely used and a popular technique to optimize the feed forward neural
network training. Traditional BP algorithm has some drawbacks, such as getting
stuck easily in local minima and slow speed of convergence. Nature inspired
meta-heuristic algorithms provide derivative-free solution to optimize complex
problems. This paper proposed a new meta-heuristic search algorithm, called
cuckoo search (CS), based on cuckoo bird’s behavior to train BP in achieving
fast convergence rate and to avoid local minima problem. The performance of
the proposed Cuckoo Search Back-Propagation (CSBP) is compared with artificial
bee colony using BP algorithm, and other hybrid variants. Specifically OR
and XOR datasets are used. The simulation results show that the computational
efficiency of BP training process is highly enhanced when coupled with the
proposed hybrid method. |
|---|