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Study on Separation Method of Magnetite Ore Based on Fuzzy Wavelet Neural Network
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Caiyong LING1, Chunrong PAN1, Kai FENG1, 2, Decan ZENG1
Mining Research and Development | 2025, 45(6) : 272 - 279
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Mining Research and Development | 2025, 45(6): 272-279
Study on Separation Method of Magnetite Ore Based on Fuzzy Wavelet Neural Network
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Caiyong LING1, Chunrong PAN1, Kai FENG1, 2, Decan ZENG1
Affiliations
  • 1.School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou, Jiangxi 341000, China
  • 2.Gannan University of Science and Technology, Ganzhou, Jiangxi 341000, China
Published: 2025-06-25
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In order to improve the separation accuracy of magnetite ore with particle size of 20−50 mm and adapt to the change of ore quality, a magnetite ore classification method based on fuzzy wavelet neural network (IGWO-FWNN) was proposed. Firstly, feature extraction was performed on the data set, and the adaptability of the model was enhanced by fuzzification and quantization. Then, the improved grey wolf algorithm was used to optimize the initial parameters of the model, which ensured the efficiency of model training. In the training process, the fuzzy rule parameters were continuously updated by the gradient descent algorithm to optimize the performance of the network model. Experiment results show that compared with the GWO-FWNN model, the accuracy of the optimized network model reaches 0.966 7, the F1 value reaches 0.951 2, and the convergence speed of the model is also improved by 24%. The results have verified the effectiveness of the proposed method on the iden tification and classification of magnetite ore, which provides a new thought for the development of magnetic separation technology.

Magnetite  /  Hall sensor  /  Fuzzy wavelet neural network  /  Improved grey wolf optimizer
凌彩勇, 潘春荣, 冯凯, 曾德灿. 基于模糊小波神经网络的磁铁矿石分选方法研究. 矿业研究与开发, 2025 , 45 (6) : 272 -279 .
Caiyong LING, Chunrong PAN, Kai FENG, Decan ZENG. Study on Separation Method of Magnetite Ore Based on Fuzzy Wavelet Neural Network[J]. Mining Research and Development, 2025 , 45 (6) : 272 -279 .
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Year 2025 volume 45 Issue 6
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  • Receive Date：2024-07-18
  • Online Date：2026-10-30
  • Published：2025-06-25
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  • Received：2024-07-18
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Affiliations
    1.School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou, Jiangxi 341000, China
    2.Gannan University of Science and Technology, Ganzhou, Jiangxi 341000, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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