Improvement of radial basis function neural network with accelerated particle swarm optimization for corrosion rate prediction of 3C steel in seawater environment

Improvement of radial basis function neural network with accelerated particle swarm optimization for corrosion rate prediction of 3C steel in seawater environment

Qingping Jian 

Chengdu Vocational & Technical College of Industry, Equipment Manufacturing College, Chengdu 610218, China

Corresponding Author Email: 
675429404@qq.com
Page: 
577-587
|
DOI: 
https://doi.org/10.3166/ACSM.42.577-587
Received: 
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Accepted: 
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Published: 
31 December 2018
| Citation

OPEN ACCESS

Abstract: 

For accurately prediction of 3C steel corrosion rate in seawater environment, this paper establishes a radial basis function neural network (RBFNN) and improves it with accelerated particle swarm optimization (APSO). Specifically, the centers, spreads and connection weights of each radial basis function (RBF) were automatically tuned by the APSO, and the number of RBFs in the RBFNN was minimized by choosing a special fitness function. The APSO-optimized RBFNN was proved through a case study to have good prediction accuracy and self-learning ability. The research findings provide an accurate, adaptive and easily-to-train prediction model for 3C steel corrosion rate in the seawater environment

Keywords: 

radial basis function neural network (RBFNN), seawater environment, accelerated particle swarm optimization (APSO), prediction model, corrosion rate

1. Introduction
2. RBFNN structure
3. The APSO
4. Prediction model based on APSO-RBFNN
5. Conclusions
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