Fuzzy serial-parallel stochastic configuration networks based on nonconvex dynamic membership function optimization

A fuzzy series–parallel stochastic configuration networks (F-SPSCN) is proposed based on the application of nonconvex optimization in fuzzy systems. Firstly, the kernel density estimation method is used to fit the distribution of original input data to generate dynamic nonconvex membership functions...

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Bibliographic Details
Published in:Information sciences Vol. 690; p. 121501
Main Authors: Qiao, Jinghui, Qiao, Jiayu, Gao, Peng, Bai, Zhe, Xiong, Ningkang
Format: Journal Article
Language:English
Published: Elsevier Inc 01.02.2025
Subjects:
Fuzzy systems
Generalized projective gradient descent algorithm
Hydrogen-based mineral phase transformation (HMPT)
Magnetic separation recovery ratio (MSRR)
Majorization-minimization algorithm
Nonconvex optimization
Stochastic configuration networks (SCN)
Nonconvex optimization
Stochastic configuration networks (SCN)
Generalized projective gradient descent algorithm
Magnetic separation recovery ratio (MSRR)
Majorization-minimization algorithm
Fuzzy systems
Hydrogen-based mineral phase transformation (HMPT)
ISSN:0020-0255
Online Access:Get full text
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Summary:A fuzzy series–parallel stochastic configuration networks (F-SPSCN) is proposed based on the application of nonconvex optimization in fuzzy systems. Firstly, the kernel density estimation method is used to fit the distribution of original input data to generate dynamic nonconvex membership functions, which enhances the fuzzy system ability to handle uncertain industrial data. Then the parameters of the nonconvex membership functions are optimized based on Majorization-Minimization algorithm and Generalized Projective Gradient Descent algorithm. The optimized membership matrices and fuzzy outputs are used as inputs of the serial-parallel stochastic configuration networks to improve the overall prediction accuracy of the model. Finally, the prediction accuracy of the F-SPSCN model has been verified by performing prediction experiments with two different functions and four benchmark datasets. The F-SPSCN model demonstrates superior performance compared to other models in predicting the magnetic separation recovery ratio (MSRR) of hydrogen-based mineral phase transformation (HMPT) process for refractory iron ore.
ISSN:0020-0255
DOI:10.1016/j.ins.2024.121501