粗糙集和支持向量机的表具识别算法研究
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摘要
针对量子粒子群算法具有陷入局部值缺点,提出了一种基于改进量子粒子群算法优化的粗糙集和支持向量机相结合的表具识别算法,引入人工蜂群算法和免疫算法,来提高算法搜索空间、收敛速度。首先通过改进量子粒子群算法优化的粗糙集对得到的特征向量进行属性约简,然后经过改进量子粒子群算法优化支持向量机参数。最后通过实验仿真表明,改进的算法能有效地减少决策属性的个数,提高了粗糙集属性约简能力,优化了支持向量机的参数,算法收敛速度快,识别准确率高。
In order to restrain particles from trapping in local optimum, this paper presents a new algorithm, which is based on the combination of rough sets and Support Vector Machine(SVM)optimized by Improved Quantum-behaved Particle Swarm Optimization(IQPSO), the artificial bee colony algorithm and immune algorithm are introduced to speed up the search space and improve the convergence speed. Firstly, the rough set optimized by IQPSO is used to reduce the attributes of the feature vector, and then the parameters of SVM are optimized by IQPSO. Finally, the simulation experiment shows that the improved algorithm can effectively reduce the number of decision attribute, improve rough set attribute reduction ability, obtain the better parameters of SVM, this algorithm has a fast convergence speed and high recognition accuracy.
In order to restrain particles from trapping in local optimum, this paper presents a new algorithm, which is based on the combination of rough sets and Support Vector Machine(SVM)optimized by Improved Quantum-behaved Particle Swarm Optimization(IQPSO), the artificial bee colony algorithm and immune algorithm are introduced to speed up the search space and improve the convergence speed. Firstly, the rough set optimized by IQPSO is used to reduce the attributes of the feature vector, and then the parameters of SVM are optimized by IQPSO. Finally, the simulation experiment shows that the improved algorithm can effectively reduce the number of decision attribute, improve rough set attribute reduction ability, obtain the better parameters of SVM, this algorithm has a fast convergence speed and high recognition accuracy.
引文
[1]高菊,叶桦.一种有效的水表数字图像二次识别算法[J].东南大学学报:自然科学版,2013,43(s1):153-157.
[2]Pawlak Z.Rough sets[J].International Journal of Computer and Information Sciences,1982,11(5):341-356.
[3]Dai J H,Li Y X.Heuristic genetic algorithm for minimal reduction in decision system based on rough set theory[C]//Proceedings of 2002 International Conference on Machine Learning and Cybernetics,2002:833-836.
[4]叶东毅,廖建坤.基于二进制粒子群优化的最小属性约简算法[J].模式识别与人工智能,2007,20(3):295-300.
[5]Ye D Y,Chen Z J,Liao J K.A new algorithm for minimum attribute reduction based on binary particle swarm optimization with vaccination[C]//Proceedings of PAKDD07,2007:1029-1036.
[6]高卫峰,刘三阳,焦合华,等.引入人工蜂群搜索算子的粒子群算法[J].控制与决策,2012,27(6):833-838.
[7]Chen X,Liu X,Jia Y.Discriminative structure selection method of Gaussian mixture models with its application to handwritten digit recognition[J].Neurocomputing,2011,74(6):954-961.
[8]胡菡静,王小妮,王磊,等.图像识别技术-神经网络识别方法的分析[J].中国新通信,2016,18(22).
[9]李战明,杨红红.车牌图像特征提取及改进神经网络的识别算法研究[J].现代电子技术,2016,39(16):102-104.
[10]刘永平,郭小波.基于最小二乘支持向量机车牌字符特征识别[J].数字技术与应用,2015(7):119-120.
[11]邵信光,杨慧中,陈刚.基于粒子群优化算法的支持向量机参数选择及其应用[J].控制理论与应用,2006,23(5):740-743.
[12]奚茂龙,孙俊,吴勇.一种二进制编码的量子粒子群优化算法[J].控制与决策,2010,25(1):99-104.
[13]Zhu G,Kwong S.Gbest-guided artificial bee colony algorithm for numerical function optimization[J].Applied Mathematics and Computation,2010,217(7):3166-3173.
[14]周辉仁,郑丕谔,赵春秀.基于遗传算法的LS-SVM参数优选及其在经济预测中的应用[J].计算机应用,2007,27(6):1418-1429.
[15]Gao Q,Liu J,Zhang H,et al.Enhanced fisher discriminant criterion for image recognition[J].Pattern Recognition,2012,45(10):3717-3724.
[2]Pawlak Z.Rough sets[J].International Journal of Computer and Information Sciences,1982,11(5):341-356.
[3]Dai J H,Li Y X.Heuristic genetic algorithm for minimal reduction in decision system based on rough set theory[C]//Proceedings of 2002 International Conference on Machine Learning and Cybernetics,2002:833-836.
[4]叶东毅,廖建坤.基于二进制粒子群优化的最小属性约简算法[J].模式识别与人工智能,2007,20(3):295-300.
[5]Ye D Y,Chen Z J,Liao J K.A new algorithm for minimum attribute reduction based on binary particle swarm optimization with vaccination[C]//Proceedings of PAKDD07,2007:1029-1036.
[6]高卫峰,刘三阳,焦合华,等.引入人工蜂群搜索算子的粒子群算法[J].控制与决策,2012,27(6):833-838.
[7]Chen X,Liu X,Jia Y.Discriminative structure selection method of Gaussian mixture models with its application to handwritten digit recognition[J].Neurocomputing,2011,74(6):954-961.
[8]胡菡静,王小妮,王磊,等.图像识别技术-神经网络识别方法的分析[J].中国新通信,2016,18(22).
[9]李战明,杨红红.车牌图像特征提取及改进神经网络的识别算法研究[J].现代电子技术,2016,39(16):102-104.
[10]刘永平,郭小波.基于最小二乘支持向量机车牌字符特征识别[J].数字技术与应用,2015(7):119-120.
[11]邵信光,杨慧中,陈刚.基于粒子群优化算法的支持向量机参数选择及其应用[J].控制理论与应用,2006,23(5):740-743.
[12]奚茂龙,孙俊,吴勇.一种二进制编码的量子粒子群优化算法[J].控制与决策,2010,25(1):99-104.
[13]Zhu G,Kwong S.Gbest-guided artificial bee colony algorithm for numerical function optimization[J].Applied Mathematics and Computation,2010,217(7):3166-3173.
[14]周辉仁,郑丕谔,赵春秀.基于遗传算法的LS-SVM参数优选及其在经济预测中的应用[J].计算机应用,2007,27(6):1418-1429.
[15]Gao Q,Liu J,Zhang H,et al.Enhanced fisher discriminant criterion for image recognition[J].Pattern Recognition,2012,45(10):3717-3724.