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CSLM: Levenberg Marquardt based Back Propagation Algorithm Optimized with Cuckoo Search
Nazri Mohd. Nawi, Abdullah Khan & M. Z. Rehman
Software and Multimedia Centre, Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM).
P.O. Box 101, 86400 Parit Raja, BatuPahat, Johor DarulTakzim, Malaysia
Email: hi100010@siwa.uthm.edu.my
Abstract. Training an artificial neural network is an optimization task, since it is desired to find optimal weight sets for a neural network during training process. Traditional training algorithms such as back propagation have some drawbacks such as getting stuck in local minima and slow speed of convergence. This study combines the best features of two algorithms; i.e. Levenberg Marquardt back propagation (LMBP) and Cuckoo Search (CS) for improving the convergence speed of artificial neural networks (ANN) training. The proposed CSLM algorithm is trained on XOR and OR datasets. The experimental results show that the proposed CSLM algorithm has better performance than other similar hybrid variants used in this study.
Keywords: artificial neural networks; back propagation; cuckoo search; levenberg marquardt; local minima.
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