Design and implementation of the data prediction model based on PSO-ELM

Xuefeng Wei; Jun Dai

doi:10.1109/ISMII52409.2021.00064

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Design and implementation of the data prediction model based on PSO-ELM

Xuefeng Wei and Jun Dai

2021 7th International Symposium on Mechatronics and Industrial Informatics (ISMII), pp.272-275

01/2021

DOI: https://doi.org/10.1109/ISMII52409.2021.00064

Handle:

https://hdl.handle.net/20.500.12741/rep:9338

Abstract

Machine learning algorithms

Mechatronics

Extreme learning machines

extreme learning machine

neural networks

Predictive models

Prediction algorithms

Data models

Particle swarm optimization

data prediction

Extreme Learning Machine (ELM) is currently a very popular type of machine learning algorithm, which is used to train a single layer feedforward neural network (SLFN). Aiming at the drawback that the ELM method cannot independently optimize the optimal network, this study uses the particle swarm algorithm to select the optimal hidden layer deviation and input weight matrix. The proposed PSO-ELM method is compared with the traditional ELM method in simulation. The accuracy of the prediction results on different gasoline octane content data has been improved. Finally, the conclusion and some potential research are summarized.

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Title: Design and implementation of the data prediction model based on PSO-ELM
Creators: Xuefeng Wei - Chongqing Three Gorges University,School of Electronic and Information Engineering,Chongqing,China
Jun Dai - State Grid Xiangyang Power Supply Company,Xiangyang,China
Academic Unit: Computer Science Department
Publisher: IEEE
Publication Details: 01/2021
Identifiers: 99257880263201671; https://hdl.handle.net/20.500.12741/rep:9338; https://doi.org/10.1109/ISMII52409.2021.00064
Language: English

Design and implementation of the data prediction model based on PSO-ELM

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