文献类型：期刊文献

英文题名：QPSO-ELM: An evolutionary extreme learning machine based on quantum-behaved particle swarm optimization

作者：Yang, Zeping[1]; Wen, Xinxiu[1]; Wang, Zhanquan[1,2]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China; [2] Department of Computer Science and Engineering, University of Minnestoa, United States

年份：2015

起止页码：69

外文期刊名：2015 7th International Conference on Advanced Computational Intelligence, ICACI 2015

收录：EI(收录号：20160301829076)

语种：英文

外文关键词：Learning systems - Particle swarm optimization (PSO) - Knowledge acquisition - Neural networks - Swarm intelligence - Learning algorithms

摘要：Extreme learning machine (ELM), as an emergent technology, has attracted tremendous attention from various fields for its fast learning speed. Different from traditional gradient-based learning algorithms for feed-forward neural networks, ELM need not be neuron alike and learns with good generalization performance. However, ELM may require more hidden neurons than traditional tuning-based learning algorithms in some applications due to the random assignment of the input weights and hidden biases. In this paper, a novel evolutionary ELM is proposed named QPSO-ELM which uses the quantum-behaved particle swarm optimization (QPSO) to select the input weights and hidden layer biases and reduces both the structural and empirical risks. The experimental results demonstrate the effectiveness of the proposed method with more compact networks. ? 2015 IEEE.