An Image Recognition Method for Coal Gangue Based on ASGS-CWOA and BP Neural Network

被引：9

作者：

Wang, Dongxing ^{[1
,2
,3
]}

Ni, Jingxiu ^{[4
]}

Du, Tingyu ^{[3
]}

机构：

[1] Zhuhai Xinhe Technol Co Ltd, R&D Dept, Zhuhai 519600, Peoples R China

[2] Zhejiang Univ, Sch Elect Engn, Hangzhou 310000, Peoples R China

[3] China Univ Min & Technol Beijing, Sch Mech Elect & Informat Engn, Beijing 100083, Peoples R China

[4] Beijing Union Univ, Comprehens Expt Teaching Demonstrat Ctr Engn, Beijing 100101, Peoples R China

来源：

SYMMETRY-BASEL | 2022年 / 14卷 / 05期

关键词：

coal gangue image; classification; wolf pack optimization; BP neural network;

D O I：

10.3390/sym14050880

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

To improve the recognition accuracy of coal gangue images with the back propagation (BP) neural network, a coal gangue image recognition method based on BP neural network and ASGS-CWOA (ASGS-CWOA-BP) was proposed, which makes two key contributions. Firstly, a new feature extraction method for the unique features of coal and gangue images is proposed, known as "Encircle-City Feature". Additionally, a method that applied ASGS-CWOA to optimize the parameters of the BP neural network was introduced to address to the issue of its low accuracy in coal gangue image recognition, and a BP neural network with a simple structure and reduced computational consumption was designed. The experimental results showed that the proposed method outperformed the other six comparison methods, with recognition of 95.47% and 94.37% in the training set and the test set, respectively, showing good symmetry.

引用

页数：16

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