Tuning  extreme  learning  machine  by  an  improved electromagnetism-like mechanism algorithm for classification problem

Mengya Zhang; Qing Wu; Zezhou Xu; Mengya Zhang; Qing Wu; Zezhou Xu

doi:10.3934/mbe.2019235

Mathematical Biosciences and Engineering

2019, Volume 16, Issue 5: 4692-4707. doi: 10.3934/mbe.2019235

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Research article Special Issues

Tuning extreme learning machine by an improved electromagnetism-like mechanism algorithm for classification problem

College of Engineering, Huazhong Agricultural University, Wuhan, Hubei, 430070, China

Received: 21 February 2019 Accepted: 13 May 2019 Published: 23 May 2019

Extreme learning machine (ELM) is a kind of learning algorithm for single hidden-layer feedforward neural network (SLFN). Compared with traditional gradient-based neural network learning algorithms, ELM has the advantages of fast learning speed, good generalization performance and easy implementation. But due to the random determination of input weights and hidden biases, ELM demands more hidden neurons and cannot guarantee the optimal network structure. Here, we report a new learning algorithm to overcome the disadvantages of ELM by tuning the input weights and hidden biases through an improved electromagnetism-like mechanism (EM) algorithm called DAEM and Moore-Penrose (MP) generalized inverse to analytically determine the output weights of ELM. In DAEM, three different solution updating strategies inspired by dragonfly algorithm (DA) are implemented. Experimental results indicate that the proposed algorithm DAEM-ELM has better generalization performance than traditional ELM and other evolutionary ELMs.
- extreme learning machine,
- electromagnetism-like mechanism,
- dragonfly algorithm,
- classification problem
Citation: Mengya Zhang, Qing Wu, Zezhou Xu. Tuning extreme learning machine by an improved electromagnetism-like mechanism algorithm for classification problem[J]. Mathematical Biosciences and Engineering, 2019, 16(5): 4692-4707. doi: 10.3934/mbe.2019235

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Abstract

Extreme learning machine (ELM) is a kind of learning algorithm for single hidden-layer feedforward neural network (SLFN). Compared with traditional gradient-based neural network learning algorithms, ELM has the advantages of fast learning speed, good generalization performance and easy implementation. But due to the random determination of input weights and hidden biases, ELM demands more hidden neurons and cannot guarantee the optimal network structure. Here, we report a new learning algorithm to overcome the disadvantages of ELM by tuning the input weights and hidden biases through an improved electromagnetism-like mechanism (EM) algorithm called DAEM and Moore-Penrose (MP) generalized inverse to analytically determine the output weights of ELM. In DAEM, three different solution updating strategies inspired by dragonfly algorithm (DA) are implemented. Experimental results indicate that the proposed algorithm DAEM-ELM has better generalization performance than traditional ELM and other evolutionary ELMs.

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