一种基于量子粒子群优化的极限学习机

doi:10.16182/j.issn1004731x.joss.201710028

系统仿真学报 ›› 2017, Vol. 29 ›› Issue (10): 2447-2458.doi: 10.16182/j.issn1004731x.joss.201710028

一种基于量子粒子群优化的极限学习机

逄珊¹, 杨欣毅², 林学森²

1.鲁东大学信息与电气工程学院,烟台 264025;
2.海军航空工程学院飞行器工程系,烟台 264001

收稿日期:2015-10-08 发布日期:2020-06-04

Evolutionary Extreme Learning Machine Optimized by Quantum-behaved Particle Swarm Optimization

Pang Shan¹, Yang Xinyi², Lin Xuesen²

1. College of Information and Electrical Engineering, Ludong University, Yantai 264025, China;
2. Department of Aircraft Engineering, Naval Aeronautical and Astronautical University, Yantai 264001, China

Received:2015-10-08 Published:2020-06-04
About author:Pang shan(1981-), female, Yantai, Shandong,master degree, lecturer, Research direction for include intelligent computation, theories and applications of machine learning.
Supported by:
National Natural Science Foundation of China (61602229), Natural Science Foundation of Shandong Province (ZR2016FQ19)

摘要/Abstract

摘要： 极限学习机(ELM)是一种新型的单隐含层神经网络的训练方法,同传统的基于梯度的网络训练方法相比,具有快速的学习速度和更好的泛化性能。ELM在实际应用中往往需要大量的隐含层神经元,由于随机设定输入权值和偏置值,容易导致病态问题的出现。为解决上述问题,提出一种应用量子粒子群(QPSO)优化包括隐含层节点个数在内的网络参数的方法。这种优化基于验证集的均方根误差,考虑到了输入权值矩阵的范数。在典型的回归和分类问题上进行试验证明了算法的有效性。

关键词: 极限学习机, 单隐含层前馈神经网络, 量子粒子群, 泛化能力

Abstract: Extreme Learning Machine (ELM) is a novel learning algorithm for Single-Hidden-Layer Feed Forward Neural Networks (SLFN) with much faster learning speed and better generalization performance than traditional gradient-based learning algorithms. However ELM tends to require more neurons in the hidden layer and lead to ill-conditioned problem due to the random selection of input weights and hidden biases. To address these problems, a learning algorithm was proposed which used quantum-behaved particle swarm optimization (QPSO) to select the optimal network parameters including the number of hidden layer neurons according to the both the root mean square error on validation data set and the norm of output weights. Experimental results on benchmark regression and classification problems have verified the performance and effectiveness of the proposed approach.

Key words: extreme learning machine, single-hidden-layer feed forward neural networks, quantum-behaved particle swarm optimization, generalization performance

中图分类号:

TP183

逄珊, 杨欣毅, 林学森. 一种基于量子粒子群优化的极限学习机[J]. 系统仿真学报, 2017, 29(10): 2447-2458.

Pang Shan, Yang Xinyi, Lin Xuesen. Evolutionary Extreme Learning Machine Optimized by Quantum-behaved Particle Swarm Optimization[J]. Journal of System Simulation, 2017, 29(10): 2447-2458.

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一种基于量子粒子群优化的极限学习机

Evolutionary Extreme Learning Machine Optimized by Quantum-behaved Particle Swarm Optimization

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