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首页> 外文期刊>Cognitive Neurodynamics >A novel algorithm with differential evolution and coral reef optimization for extreme learning machine training
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A novel algorithm with differential evolution and coral reef optimization for extreme learning machine training

机译：具有极限进化和珊瑚礁优化的新算法，用于极限学习机训练

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摘要

Extreme learning machine (ELM) is a novel and fast learning method to train single layer feed-forward networks. However due to the demand for larger number of hidden neurons, the prediction speed of ELM is not fast enough. An evolutionary based ELM with differential evolution (DE) has been proposed to reduce the prediction time of original ELM. But it may still get stuck at local optima. In this paper, a novel algorithm hybridizing DE and metaheuristic coral reef optimization (CRO), which is called differential evolution coral reef optimization (DECRO), is proposed to balance the explorative power and exploitive power to reach better performance. The thought and the implement of DECRO algorithm are discussed in this article with detail. DE, CRO and DECRO are applied to ELM training respectively. Experimental results show that DECRO-ELM can reduce the prediction time of original ELM, and obtain better performance for training ELM than both DE and CRO.
机译：极限学习机（ELM）是一种新颖的快速学习方法，用于训练单层前馈网络。但是，由于需要大量隐藏神经元，因此ELM的预测速度不够快。为了减少原始ELM的预测时间，提出了一种基于差分演化（DE）的基于演化的ELM。但是它可能仍然会陷入局部最优状态。在本文中，提出了一种将DE与超启发式珊瑚礁优化（CRO）混合的新算法，称为差异进化珊瑚礁优化（DECRO），以平衡开发能力和开发能力，以达到更好的性能。本文详细讨论了DECRO算法的思想和实现。 DE，CRO和DECRO分别应用于ELM培训。实验结果表明，与DE和CRO相比，DECRO-ELM可以减少原始ELM的预测时间，并获得更好的ELM训练性能。

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