期刊
出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.protcy.2013.12.157
关键词
Artificial Neural Network; back propagation; local minima; Levenberg Marquardt; Cuckoo Search
Back propagation training algorithm is widely used techniques in artificial neural network and is also very popular optimization task in finding an optimal weight sets during the training process. However, traditional back propagation algorithms have some drawbacks such as getting stuck in local minimum and slow speed of convergence. This research proposed an improved Levenberg Marquardt (LM) based back propagation (BP) trained with Cuckoo search algorithm for fast and improved convergence speed of the hybrid neural networks learning method. The performance of the proposed algorithm is compared with Artificial Bee Colony (ABC) and the other hybridized procedure of its kind. The simulation outcomes show that the proposed algorithm performed better than other algorithm used in this study in term of convergence speed and rate. (C) 2013 The Authors. Published by Elsevier Ltd.
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