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Algorithms 2018, 11(11), 174; https://doi.org/10.3390/a11110174

Local Coupled Extreme Learning Machine Based on Particle Swarm Optimization

1
School of Mathematics and Statistics, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
2
School of Control Science and Engineering, Shandong University, Jinan 250061, China
*
Author to whom correspondence should be addressed.
Received: 20 August 2018 / Revised: 16 October 2018 / Accepted: 29 October 2018 / Published: 1 November 2018
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Abstract

We developed a new method of intelligent optimum strategy for a local coupled extreme learning machine (LC-ELM). In this method, both the weights and biases between the input layer and the hidden layer, as well as the addresses and radiuses in the local coupled parameters, are determined and optimized based on the particle swarm optimization (PSO) algorithm. Compared with extreme learning machine (ELM), LC-ELM and extreme learning machine based on particle optimization (PSO-ELM) that have the same network size or compact network configuration, simulation results in terms of regression and classification benchmark problems show that the proposed algorithm, which is called LC-PSO-ELM, has improved generalization performance and robustness. View Full-Text
Keywords: extreme learning machine; LC-ELM; particle swarm optimization; LC-PSO-ELM extreme learning machine; LC-ELM; particle swarm optimization; LC-PSO-ELM
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Guo, H.; Li, B.; Li, W.; Qiao, F.; Rong, X.; Li, Y. Local Coupled Extreme Learning Machine Based on Particle Swarm Optimization. Algorithms 2018, 11, 174.

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