改进自适应遗传神经网络在混合气体识别中的应用
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摘要
BP神经网络和遗传神经网络是混合气体识别中常用的方法,但在实际应用仍然存在一些缺陷与不足。针对存在的问题,提出了1种改进自适应遗传算法,该算法根据进化过程种群中未产生更优解的代数,自适应调整变异率和变异量。利用该改进自适应遗传算法优化BP神经网络的连接权和阈值,构成改进自适应遗传神经网络,并应用于混合气体的识别中。实验结果表明:改进自适应遗传神经网络收敛成功率由40%提高到80%,平均识别误差H_2S由4.66 mL/m~3降为3.69 mL/m~3,CH_4由17.14 mL/m~3降为15.77 mL/m~3,CO由4.38 mL/m~3降为4.19 mL/m~3。
BP neural network and GA neural network were the common methods applied in multi-gas recognition.But the methods were found having some defects and shortages in practical use.According to the shortages,an improved adaptive genetic algorithm which can adaptively adjust mutation probabilities and volumes based on generations having no better solution in evolution is proposed.This algorithm is used to optimize connection weights and thresholds of BP neural network and applied in multi-gas recognition.The experimental results show that the convergence probability of this improved adaptive genetic neural network is increased from 40%to 80%,and the average recognition errors, H_2S is reduced from 4.66 mL/m~3 to 3.69 mL/m~3,CH_4 is reduced from 17.14 mL/m~3 to 15.77 mL/m~3,CO is reduced from 4.38 mL/m~3 to 4.19 mL/m~3.
BP neural network and GA neural network were the common methods applied in multi-gas recognition.But the methods were found having some defects and shortages in practical use.According to the shortages,an improved adaptive genetic algorithm which can adaptively adjust mutation probabilities and volumes based on generations having no better solution in evolution is proposed.This algorithm is used to optimize connection weights and thresholds of BP neural network and applied in multi-gas recognition.The experimental results show that the convergence probability of this improved adaptive genetic neural network is increased from 40%to 80%,and the average recognition errors, H_2S is reduced from 4.66 mL/m~3 to 3.69 mL/m~3,CH_4 is reduced from 17.14 mL/m~3 to 15.77 mL/m~3,CO is reduced from 4.38 mL/m~3 to 4.19 mL/m~3.
引文
1 Yang Li and Wang Lei.Study on detection of toxic gases in traffic enviroment.Computer Engineering and Applications,2010, 46(8):202-204.
2 Lv Pin.Study on Key Detecting Teehnologies in Indoor Air Quality Controlling.Dalian.Dalian University of Technology, 2008.
3 Song Haisheng,Su Xiaoyun and Zhao Xiaolin.Intelligent electronic nose system based on Lab VIEW platform.Transducer and Microsystem Technologies,2012,31(4):86-88.
4 Wang He,Zhou Dongxiang and Song Weiyuan.Recognition of multi-gas by using genetic algorithm optimizing neural network. Journal of Huazhong University of Science and Technology (Natural Science Edition),2007,35(9):118-120.
5 Li Yijun.The Research of the PID Auto-tuning Algorithm Based on GA Neural Network.Xian:Xian University of Science and Technology,2009.
6 Ye Bing.An Arbitrary Angle Measurement System Based on Genetic and Neural Network Real-time Error Correction.Hefei: Hefei University of Technology,2003.
7 Gu Jingjing.A Thesis in Communication and Information System Engineering.Nanjing:Nanjing University of Aeronautics and Astronautics,2010.
8 Srinivas M and Patnaik L M.Adaptive probabilities of crossover and mutation in genetic algorithms.IEEE Trans.On Systems,Man and Cybernetics,1994,24(4):656-665.
9 Zheng Zhijun and Zheng Shouqi.Evolving neural network by an adaptive real-coded genetic algorithm.Computer Engineering and Applications,2000,36(9):36-37.
10 Zhang Shen,Chen Junquan and Zhang Dian.Gob spontaneous combustion prediction based on neural network.Transducer and Microsystem Technologies,2012,31(5):10-12.
11 Adem E A and Melih G K.A PWR reload optimisation code (XCore) using artificial neural networks and genetic algorithms. Annals of Nuclear Energy,2003,30:35-53.
12 Xu Pengfei.Study on Earthquake Prediction Using GA Neural Network.Hefei:Anhui University,2010.
1杨莉,王磊.交通环境有毒气体的检测技术研究[J].计算机工程与应用,2010,46(8):202-204.
2吕品.室内空气质量控制中关键检测技术的研究[D].大连:大连理工大学,2008.
3宋海声,苏小芸,赵晓林.基于LabVIEW平台的智能电子鼻系统[J].传感器与微系统,2012,31(4):86-88.
4王鹤,周东祥,宋委远,等.基于遗传神经网络的混合气体识别研究[J].华中科技大学学报(自然科学版),2007, 35(9):118-120.
5李一军.基于遗传神经网络的PID自整定算法的研究[D].西安:西安科技大学,2009.
6叶兵.基于遗传神经网络模型实时误差修正任意角测量系统[D].合肥:合肥工业大学,2003.
7顾晶晶.遗传神经网络在车牌识别系统中研究及应用[D].南京:南京航空航天大学,2010.
9郑志军,郑守淇.用基于实数编码的自适应遗传算法进化神经网络[J].计算机工程与应用,2000,36(9):36-37.
10张申,陈军权,张典.基于神经网络的采空区自燃预测[J].传感器与微系统,2012,31(5):10-12.
12徐鹏飞.利用遗传神经网络技术对地震预测的研究[D].合肥:安徽大学,2010.
2 Lv Pin.Study on Key Detecting Teehnologies in Indoor Air Quality Controlling.Dalian.Dalian University of Technology, 2008.
3 Song Haisheng,Su Xiaoyun and Zhao Xiaolin.Intelligent electronic nose system based on Lab VIEW platform.Transducer and Microsystem Technologies,2012,31(4):86-88.
4 Wang He,Zhou Dongxiang and Song Weiyuan.Recognition of multi-gas by using genetic algorithm optimizing neural network. Journal of Huazhong University of Science and Technology (Natural Science Edition),2007,35(9):118-120.
5 Li Yijun.The Research of the PID Auto-tuning Algorithm Based on GA Neural Network.Xian:Xian University of Science and Technology,2009.
6 Ye Bing.An Arbitrary Angle Measurement System Based on Genetic and Neural Network Real-time Error Correction.Hefei: Hefei University of Technology,2003.
7 Gu Jingjing.A Thesis in Communication and Information System Engineering.Nanjing:Nanjing University of Aeronautics and Astronautics,2010.
8 Srinivas M and Patnaik L M.Adaptive probabilities of crossover and mutation in genetic algorithms.IEEE Trans.On Systems,Man and Cybernetics,1994,24(4):656-665.
9 Zheng Zhijun and Zheng Shouqi.Evolving neural network by an adaptive real-coded genetic algorithm.Computer Engineering and Applications,2000,36(9):36-37.
10 Zhang Shen,Chen Junquan and Zhang Dian.Gob spontaneous combustion prediction based on neural network.Transducer and Microsystem Technologies,2012,31(5):10-12.
11 Adem E A and Melih G K.A PWR reload optimisation code (XCore) using artificial neural networks and genetic algorithms. Annals of Nuclear Energy,2003,30:35-53.
12 Xu Pengfei.Study on Earthquake Prediction Using GA Neural Network.Hefei:Anhui University,2010.
1杨莉,王磊.交通环境有毒气体的检测技术研究[J].计算机工程与应用,2010,46(8):202-204.
2吕品.室内空气质量控制中关键检测技术的研究[D].大连:大连理工大学,2008.
3宋海声,苏小芸,赵晓林.基于LabVIEW平台的智能电子鼻系统[J].传感器与微系统,2012,31(4):86-88.
4王鹤,周东祥,宋委远,等.基于遗传神经网络的混合气体识别研究[J].华中科技大学学报(自然科学版),2007, 35(9):118-120.
5李一军.基于遗传神经网络的PID自整定算法的研究[D].西安:西安科技大学,2009.
6叶兵.基于遗传神经网络模型实时误差修正任意角测量系统[D].合肥:合肥工业大学,2003.
7顾晶晶.遗传神经网络在车牌识别系统中研究及应用[D].南京:南京航空航天大学,2010.
9郑志军,郑守淇.用基于实数编码的自适应遗传算法进化神经网络[J].计算机工程与应用,2000,36(9):36-37.
10张申,陈军权,张典.基于神经网络的采空区自燃预测[J].传感器与微系统,2012,31(5):10-12.
12徐鹏飞.利用遗传神经网络技术对地震预测的研究[D].合肥:安徽大学,2010.
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