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

Authors

Pang Shan, 1. College of Information and Electrical Engineering, Ludong University, Yantai 264025, China;;
Xinyi Yang, 2. Department of Aircraft Engineering, Naval Aeronautical and Astronautical University, Yantai 264001, China;
Xuesen Lin, 2. Department of Aircraft Engineering, Naval Aeronautical and Astronautical University, Yantai 264001, China;

Abstract

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.

Recommended Citation

Shan, Pang; Yang, Xinyi; and Lin, Xuesen (2020) "Evolutionary Extreme Learning Machine Optimized by Quantum-behaved Particle Swarm Optimization," Journal of System Simulation: Vol. 29: Iss. 10, Article 28.
DOI: 10.16182/j.issn1004731x.joss.201710028
Available at: https://dc-china-simulation.researchcommons.org/journal/vol29/iss10/28

First Page

2447

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201710028

Last Page

2458

CLC

TP183

Recommended Citation

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.

DOI

10.16182/j.issn1004731x.joss.201710028