A new Cuckoo Search Based Levenberg-Marquardt (CSLM) algorithm
Back propagation neural network (BPNN) algorithm is a widely used technique in training artificial neural networks. It is also a very popular optimization procedure applied to find optimal weights in a training process. However, traditional back propagation optimized with Levenberg marquardt trainin...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Published: |
2013
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/4004/ http://eprints.uthm.edu.my/4004/1/A_New_Cuckoo_Search_Based_Levenberg%2DMarquardt.pdf |
| Summary: | Back propagation neural network (BPNN) algorithm is a widely used
technique in training artificial neural networks. It is also a very popular optimization
procedure applied to find optimal weights in a training process. However,
traditional back propagation optimized with Levenberg marquardt training
algorithm has some drawbacks such as getting stuck in local minima, and network
stagnancy. This paper proposed an improved Levenberg-Marquardt back
propagation (LMBP) algorithm integrated and trained with Cuckoo Search (CS)
algorithm to avoided local minima problem and achieves fast convergence. The
performance of the proposed Cuckoo Search Levenberg-Marquardt (CSLM) algorithm
is compared with Artificial Bee Colony (ABC) and similar hybrid variants.
The simulation results show that the proposed CSLM algorithm performs
better than other algorithm used in this study in term of convergence rate and
accuracy. |
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