GA-BP神经网络对片烟结构的预测研究

doi:10.16180/j.cnki.issn1007-7820.2022.06.006

Abstract

Abstract:

In view of the problem that it is difficult to predict the structure of tobacco during the threshing process of redrying plant, a GA-BP neural network prediction model based on MATLAB image processing is proposed. For the classification of tobacco leaf, the obtained tobacco leaf images are preprocessed using of the MATLAB software. Then, the main characteristic variables that measure the structure of the tobacco are extracted, and industry standards and cluster analysis algorithms are used to classify the data. Through the standard mathematical method of statistics, the BP neural network prediction model optimized by genetic algorithm is constructed to predict and optimize the main influencing parameters. The research results show that the method proposed in this study has high prediction accuracy, and the prediction range is less than 0.059, which indicates that the method can effectively solve the problem of prediction of slices of tobacco in the process of threshing.

Key words: MATLAB, image processing, cluster analysis, leave-one-out-cross-validation, BP neural network, genetic algorithm, GA-BP neural network, binarization

CLC Number:

TP29

ZHANG Chongchong,HUANG Yayu. A GA-BP Neural Network for Predicting the Structure of Leaf Tobacco[J].Electronic Science and Technology, 2022, 35(6): 35-42.

Figures/Tables 9

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Table 1.

Figure 5.

Figure 6.

Figure 7.

Table 2.

References 19

[1]	杨丹, 赵海滨, 龙哲, 等. MATLAB图像处理实例详解[M]. 北京: 清华大学出版社, 2013.
	Yang Dan, Zhao Haibin, Long Zhe, et al. Detailed explanation of MATLAB image processing example[M]. Beijing: Tsinghua University Press, 2013.
[2]	张泽旭. 神经网络控制与MATLAB仿真[M]. 哈尔滨: 哈尔滨工业大学出版社, 2011.
	Zhang Zexu. Neural network control and MATLAB simulation[M]. Harbin: Harbin Institute of Technology Press, 2011.
[3]	MATLAB中文论坛. MATLAB神经网络30个案例分析[M]. 北京: 北京航空航天大学出版社, 2010.
	MATLAB Chinese Forum. Analysis of 30 cases of MATLAB neural network[M]. Beijing: Beihang University Press, 2010.
[4]	赖安卿, 刘云鹏, 付尧明, 等. 振荡燃烧火焰图像处理[J]. 燃烧科学与技术, 2020, 26(1):10-17.
	Lai Anqing, Liu Yunpeng, Fu Yaoming, et al. Image processing of combustion oscillating flame[J]. Journal of Combustion Science and Technology, 2020, 26(1):10-17.
[5]	杨玮, 兰红, 李民赞, 等. 基于图像处理和GBRT模型的表土层土壤容重预测[J]. 农业机械学报, 2020, 51(9):193-200.
	Yang Wei, Lan Hong, Li Minzan, et al. Prediction of top soil layer bulk density based on image processing and gradient boosting regression tree model[J]. Transactions of the Chinese Society for Agricultural Machinery, 2020, 51(9):193-200.
[6]	刘慧力, 贾洪雷, 王刚, 等. 基于深度学习与图像处理的玉米茎秆识别方法与试验[J]. 农业机械学报, 2020, 51(4):207-215.
	Liu Huili, Jia Honglei, Wang Gang, et al. Method and experiment of maize(Zea Mays L.) stems recognition based on deep learning and image processing[J]. Transactions of the Chinese Society for Agricultural Machinery, 2020, 51(4):207-215.
[7]	郁浩, 张荣福, 程金光, 等. 基于图像处理的钢包标牌识别方法[J]. 电子科技, 2016, 29(1):94-97.
	Yu Hao, Zhang Rongfu, Cheng Jinguang, et al. Ladle license plate recognition based on image processing[J]. Electronic Science and Technology, 2016, 29(1):94-97.
[8]	Gonzalez R C, Woods R E, Eddins S L. 数字图像处理[M]. 阮秋琦,译. 北京: 电子工业出版社, 2005.
	Gonzalez R C, Woods R E, Eddins S L. Digital image processing[M].Translated by Ruan Qiuqi. Beijing: Electronic Industry Press, 2005.
[9]	陈朋. 烟叶纹理表面的视觉检测技术研究与应用[D]. 贵州: 贵州大学, 2017.
	Chen Peng. Research and application of visual inspection technology for texture of tabacco surface[D]. Guizhou: Guizhou University, 2017.
[10]	李伟, 张长胜, 冯广, 等. 基于图像处理的指针式表盘自动读取系统设计[J]. 电子科技, 2018, 31(6):24-26.
	Li Wei, Zhang Changsheng, Feng Guang, et al. The design of automatic system read pointer based on image processing[J]. Electronic Science and Technology, 2018, 31(6):24-26.
[11]	蒙西, 乔俊飞, 韩红桂. 基于类脑模块化神经网络的污水处理过程关键出水参数软测量[J]. 自动化学报, 2019, 45(5):906-919.
	Meng Xi, Qiao Junfei, Han Honggui. Soft measurement of key effluent parameters in wastewater treatment process using brain-like modular neural network[J]. Acta Automatica Sinica, 2019, 45(5):906-919.
[12]	辛焕平. MATLAB R2017a模式识别与智能计算[M]. 北京: 电子工业出版社, 2018.
	Xin Huanping. MATLAB R2017a pattern recognition and intelligent computing[M]. Beijing: Electronic Industry Press,2018.
[13]	Zhao Y F, Fu Z G. On-Line monitoring of coal calorific value based on GA-BP[J]. Advances in Energy and Power Engineering, 2016, 4(4):103-110. doi: 10.12677/AEPE.2016.44014
[14]	吕升, 郭迎清, 孙浩. 基于GA-AANN神经网络的SDQ算法的航空发动机传感器数据预处理[J]. 推进技术, 2018, 39(5):1142-1150.
	Lü Sheng, Guo Yingqing, Sun Hao. Aero-engine sensor data preprocessing based on GA-AANN neural network SDQ algorithm[J]. Journal of Propulsion Technology, 2018, 39(5):1142-1150.
[15]	许永, 李超, 杨乾栩, 等. 基于BP神经网络建立初烤烟叶主流烟气中氢氰酸释放量的预测模型[J]. 云南农业大学学报(自然科学版), 2018, 33(1):72-78.
	Xu Yong, Li Chao, Yang Qianxu, et al. Establishment of Predicting model of hydrogen cyanide contents in flue-cured tobacco mainstream smoke based on BP neural network[J]. Journal of Yunnan Agricultural University(Natural Sciences), 2018, 33(1):72-78.
[16]	秦兴, 高晓琪, 陈滨. 基于压缩卷积神经网络的图像超分辨率算法[J]. 电子科技, 2020, 33(5):1-8.
	Qin Xing, Gao Xiaoqi, Chen Bin. Image super-resolution algorithm based on squeezenet convolution neural network[J]. Electronic Science and Technology, 2020, 33(5):1-8.
[17]	都月, 孟晓辰, 祝连庆. 遗传算法和神经网络的重叠光谱解析[J]. 光谱学与光谱分析, 2020, 40(7):2066-2072.
	Du Yue, Meng Xiaochen, Zhu Lianqing. Overlapping spectrum analysis based on genetic algorithm and BP neural network[J]. Spectroscopy and Spectral Analysis, 2020, 40(7):2066-2072.
[18]	顾迎捷, 桂小林, 李德福, 等. 基于神经网络的机器阅读理解综述[J]. 软件学报, 2020, 31(7):2095-2126.
	Gu Yingjie, Gui Xiaolin, Li Defu, et al. Survey of machine reading comprehension based on neural network[J]. Journal of Software, 2020, 31(7):2095-2126.
[19]	雷宇, 李孝安. 一种基于GA-BP学习的神经网络推理机制的研究[J]. 电子设计工程, 2013, 21(12):34-36.
	Lei Yu, Li Xiaoan. Study on GA-BP-based neural network inference mechanism[J]. Electronic Design Engineering, 2013, 21(12):34-36.

		训练样本	验证集	测试集	极差
BP	所有特征	98.294%	97.824%	97.126%	59.000×10^-3
BP	理想特征	98.897%	98.555%	98.973%	17.461×10^-3
GA-BP	所有特征	98.781%	98.346%	98.753%	4.060×10^-3
GA-BP	理想特征	99.851%	99.843%	99.836%	4.733×10^-3

A GA-BP Neural Network for Predicting the Structure of Leaf Tobacco

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 19

Related Articles 15

Metrics

Comments

Recommended 10

[1]	Sunyun YANG,Xiu KAN. Design of Biological Behavior Analysis System Based on Vision and IMU Sensors [J]. Electronic Science and Technology, 2022, 35(4): 28-34.
[2]	Li HUA,Jian YANG,Tianchen YUAN,Ruigang SONG. Track Disease Diagnosis Method Based on VMD and BP Neural Network [J]. Electronic Science and Technology, 2022, 35(4): 40-46.
[3]	Lingyu JI,Yongbin GAO,Chenglu ZHAO,Xianhua TANG,Kaicheng XU,Jiacheng XU. CTA Segmentation Algorithm of Abdominal Artery Based on 3D Fully Convolutional Network [J]. Electronic Science and Technology, 2022, 35(3): 38-44.
[4]	Meiyi QI,Aihua LIAO. Design of Reliability Evaluation System of Traction Motor Rolling Bearing Based on MATLAB App Designer [J]. Electronic Science and Technology, 2022, 35(3): 79-86.
[5]	JIN Xiao,WU Fei,YAN Song,LU Wenxia,ZHANG Zhongyi. Fingerprint Location Method of Metro Station Based on GAWK-means [J]. Electronic Science and Technology, 2022, 35(2): 34-39.
[6]	LIU Chenmin,WANG Yagang. Optimization of Firefly Algorithm Based on Continuous Space [J]. Electronic Science and Technology, 2022, 35(2): 40-45.
[7]	SHAO Zhihui,YANG Jian,YUAN Tianchen,WU Weijia. Sleeper Diseases Diagnosis Based on Permutation Entropy and Support Vector Machine [J]. Electronic Science and Technology, 2022, 35(2): 52-58.
[8]	LIU Jianlong,HAO Zhenghang. Comparative Study of Wind Power System Simulation Based on Back-to-Back Converters [J]. Electronic Science and Technology, 2022, 35(2): 67-73.
[9]	ZONG Shengkang,CHENG Jianpeng,ZHANG Xiliang. Automatic Detection Method of Crane Track Altitude Difference Based on Spot Position [J]. Electronic Science and Technology, 2022, 35(1): 21-28.
[10]	NIU Shuaichen,WANG Fuzhong,HAN Yaofei,HE Guofeng,NIU Yeke. Control Strategy of Modular Multilevel Converter Under Unbalanced Grid Voltage [J]. Electronic Science and Technology, 2022, 35(1): 80-86.
[11]	XIU Xiaobo,LI Boquan,ZHOU Feng. Optimization of Temperature Sensor Location Based on Genetic Algorithm [J]. Electronic Science and Technology, 2021, 34(9): 17-23.
[12]	WANG Yang,WANG Yagang. Identification Method Based on Step Response and Genetic Algorithm to Optimize Higher-Order Plus Time-Delay Model [J]. Electronic Science and Technology, 2021, 34(9): 41-46.
[13]	LI Yudong,LIAN Haishan,HU Xiaodan. AC/AC Frequency Conversion Power Supply Based on Three-Phase Input [J]. Electronic Science and Technology, 2021, 34(9): 47-53.
[14]	YAN Zhenbin,ZHENG Bochao,ZHOU Zhiyong. Research on Grid Connection of Photovoltaic Power Generation Systems Based on a New Robust Droop Control Method [J]. Electronic Science and Technology, 2021, 34(8): 79-86.
[15]	MENG Xiaojiao,ZHANG Shiwei,LI Xiaojian,LI Minyue,SONG Bingxin,LU Hongmin. An Evaluation Method of Vehicle-Mounted Communication Equipment Performance Based on BP Neural Network [J]. Electronic Science and Technology, 2021, 34(5): 24-28.