Design of prediction model for multiple feature-type problems based on improved PSO-RBF neural network-Reference-Cited by-同舟云学术

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Design of prediction model for multiple feature-type problems based on improved PSO-RBF neural network

Published:2023-09-23 Issue: Volume:32 Page:22-27
ISSN:
Container-title:2023 IEEE 6th International Conference on Information Systems and Computer Aided Education (ICISCAE)
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Author:

Yang Ruiqi¹,Shang Yujing²,Yao Jiangxiu³,Su Rui⁴,Zhou Junjie³,Tang Liulu⁵

Affiliation:

1. Wuhan University of Science and Technology,School of Mechanical Automation,Wuhan,China

2. Hefei University of Technology,School of Computer and Information Engineering,Xuancheng,China

3. Wuxi Taihu University,School of Intelligent Equipment Engineering,Wuxi,China

4. Changzhou University,School of Urban Construction,Changzhou,China

5. Nanjing University,Business School,Nanjing,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10391857/10391858/10393737.pdf?arnumber=10393737

Reference8 articles.

1. Forecasting cargo growth and regional role of the port of Hong Kong

2. Application of Subset Autoregressive Integrated Moving Average Model for Short-Term Freeway Traffic Volume Forecasting

3. Non-linear dimensionality reduction techniques for unsupervised feature extraction

4. Bi-level dimensionality reduction methods using feature selection and feature extraction

5. Dimensionality reduction for data in multiple feature representations;Lin

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