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Conditioning optimization of extreme learning machine by multitask beetle antennae swarm algorithm

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Abstract

Extreme learning machine (ELM) as a simple and rapid neural network has been shown its good performance in various areas. Different from the general single hidden layer feedforward neural network (SLFN), the input weights and biases in hidden layer of ELM are generated randomly, so that it only takes a little computational overhead to train the model. However, the strategy of selecting input weights and biases at random may result in ill-conditioned problems. Aiming to optimize the conditioning of ELM, we propose an effective particle swarm heuristic algorithm called Multitask Beetle Antennae Swarm Algorithm (MBAS), which is inspired by the structures of artificial bee colony (ABC) algorithm and Beetle Antennae Search (BAS) algorithm. Then, the proposed MBAS is applied for optimizing the input weights and biases of ELM to solve its ill-conditioned problems. Experiment results show that the proposed method is capable of simultaneously reducing the condition number and regression error, and achieving good generalization performance.

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Acknowledgements

This work is supported in part by the Guangdong Graduate Education Innovation Project (No. 2020XSLT16), Science and Technology Project of Guangdong Province (No. 2019A050513011), National Nature Science Foundation of China (No. U1701266), and Guangdong Provincial Key Laboratory of Intellectual Property and Big Data under Grant (No. 2018B030322016).

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Authors and Affiliations

  1. School of Information Engineering, Guangdong University of Technology, Guangzhou, 510006, China

    Xixian Zhang, Zhijing Yang, Faxian Cao, Jiangzhong Cao, Meilin Wang & Nian Cai

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  1. Xixian Zhang
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  2. Zhijing Yang
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  3. Faxian Cao
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  4. Jiangzhong Cao
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  5. Meilin Wang
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  6. Nian Cai
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Correspondence to Zhijing Yang.

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Zhang, X., Yang, Z., Cao, F. et al. Conditioning optimization of extreme learning machine by multitask beetle antennae swarm algorithm. Memetic Comp. 12, 151–164 (2020). https://doi.org/10.1007/s12293-020-00301-w

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