小波分析在害虫预测中的应用研究
|
摘要
如何提高害虫预测预报特别是中长期预测预报的准确率,始终是农作物害虫预测预报工作中的核心和热点问题。由于生态系统本身所固有的复杂性特点,造成害虫种群动态变化表现出不均匀性、差异性、多样性、突发性和随机性等特征,导致了害虫的预测预报在准确率方面或多或少的存在着不尽如人意。近年来,得益于预测相关理论和信息技术的进步,农作物害虫预测预报技术也有了长足的发展,其中,将传统的线性理论与非线性科学中的分形、神经网络、混沌和小波分析等理论有机结合,建立各学科相互融合的方法论,实现害虫发生的分析与描述,建立快速准确的预测预报体系,是害虫预测预报发展的新方向之一。目前,神经网络、混沌、分形在害虫预测预报中已有应用,而作为非线性理论重要组成部分的小波分析理论在害虫预测领域尚未有应用。
本论文借助于小波分析理论的发展,首次将小波分析方法系统地引入到农作物害虫预测预报领域,利用小波分析时频局部化好的优势对害虫发生进行了精细化的分析,基于害虫发生的历史调查数据研究了如何利用小波方法分析害虫发生的特征和规律,深入探讨了小波分析如何与时间序列分析、神经网络相结合,以实例分析为基础建立了新的害虫预测模型,并对模型进行了检验。概括起来,本论文主要研究结果如下：
1、基于小波分析的方法,对害虫年际和年代际发生规律进行分析,通过对郓城县2、3、4代棉铃虫年卵量时间序列、烟台市1、2代玉米螟年诱蛾量时间序列以及济宁市、文登市2代粘虫年诱蛾量时间序列的实例分析结果表明：小波分析可以清晰的挖掘出害虫发生的年际和年代际周期,分析确定其中的主要周期,展现各演变周期中害虫数量的高低波动、分布变化以及周期突变点、周期拐点等特征；基于多分辨率分析的方法,对害虫的短期变化特征进行分析,通过临清市1995年、汶上县1994年棉铃虫逐日诱蛾量时间序列以及招远市2008年金纹细蛾逐日诱蛾量时间序列的实例分析结果表明：小波分析能够对害虫在一年中的世代更替、发生高峰等特征进行准确定位。
2、利用Lipschitz指数对害虫发生时间序列的周期突变点进行奇异性分析,通过对烟台市1代玉米螟年蛾量时间序列和郓城县二代棉铃虫年卵量时间序列中部分周期突变点的实例分析结果表明：结合小波变换所得到的Lipschitz指数能够揭示害虫发生时间序列中各周期突变点不同的奇异性特征并衡量了各突变点的奇异性大小。
3、将小波分解应用于害虫发生非平稳时间序列的分析和预测,通过小波分解,将非平稳时间序列分离为多个分量,然后分别针对各分量进行分析和建模,最后将各分量模型进行组合,从而得到原非平稳时间序列的预测模型。通过对烟台市一代玉米螟发生程度非平稳时间序列和文登市一代玉米螟蛾量非平稳时间序列进行的实例分析建模表明：小波分析适合于处理非平稳时间序列,对于那些采用传统非平稳时间序列分析难以完成建模的害虫发生非平稳时间序列,该方法能够很好的完成建模。
4、将径向基小波网络首次引入农作物害虫预测预报领域,并改进了径向基小波网络的学习算法,使之适合于害虫预测的应用。以惠民县棉铃虫的监测数据为基础建立的2代棉铃虫卵量峰值日期9维输入1维输出径向基小波网络模型、以鲁西棉区棉铃虫监测数据为基础建立的2代棉铃虫发生程度6维输入1维输出径向基小波网络模型、以德州市玉米螟监测数据为基础建立的1代玉米螟发生程度5维输入4维输出径向基小波网络模型,并分别利用5年的实况数据对上述3个模型的预测效果进行了检验,检验结果表明上述3个模型的预测效果令人满意。
5、基于cmorl-1复小波变换和统计分析的相关分析,对害虫发生与气象因素之间的多尺度相关性进行了研究和初步探索。通过对惠民县2代棉铃虫卵量数据序列和气象因子数据序列、烟台市1代玉米螟蛾量数据序列和气象因子的实例分析结果表明：害虫发生与气象因素之间存在着多尺度的复杂相关性,而采用小波分析结合统计相关的多尺度相关分析,则可以这种复杂相关,从而为分析和建立预测模型提供参考。
总之,本文运用新理论、新方法,进行了农作物害虫预测预报新方法的研究,对如何提高农作物害虫预测预报的准确率作了积极的探索。
How to improve pest forecast, especially the accuracy of long-term pest forecast, always was the core of the work and hot spots at pest forecast. The pest population dynamics showed heterogeneity, difference, diversity, and other characteristics of sudden and random because of the ecosystem complexity inherent characteristics, these characteristics led to that the accuracy of pest forecast was unsatisfactory. In recent years, thanks to development of forecasting theory and information technology, crop pest forecast technology has made great progress. Among them, it is the new direction of development of pest forecast that combining the traditional linear theory and nonlinear science including fractal, neural network, chaos theory and wavelet analysis etc, establishing the methodology of merging various disciplines, realizing the analysis and description of pests and establishing the fast and accurate prediction forecasting system. Currently, neural networks, chaos, fractal prediction have been applied in the pest forecasting, but wavelet analysis theory, as an important component of non-linear theory application has not been applied in pest forecasting.
This paper, based on development of wavelets analysis theory,-introduced wavelets analysis method into the area of pest forecasting for the first time, analyzed pest occurrence in detail with the good advantage of wavelets in time-frequency localization. This paper, studied how to use wavelets method to analyze the rules and characteristics of pest occurrence based on historical investigation data of pest, explored deeply how to combine wavelets analysis and time series analysis, neural network. This paper built the new models of pest forecasting based on instances analysis, tested the models. In summary, the major findings are as follows:
Firstly, based on wavelet analysis method, this paper analyzed inter-annual and inter-decadal rules of pests. According to the cases studying for annual oviposition quantity time series of the first, second, third generation Helicoverpa armigera in Yuncheng, annual moths quantity time series of the first, second generation Ostrinia furnacalis in Yantai, annual moths quantity time series of the second generation Mythimna separata in Jining and Wendeng, this paper indicated that wavelet analysis can dig out inter-annual and inter-decadal cycle of pests clearly, determine which of the main cycle, show the evolution and fluctuation of the cycle, cycle features such as mutation points and turnning points. On the other hand, this paper analyzed short-term rules of pests based on multi-resolution analysis. According the cases studying for daily moths quantity time series trapped by light of H. armigera at1995in Linqing and at1994in Wenshang, daily moths quantity time series trapped by light of Lithocolletis ringoniella at2008in Zhaoyuan, this paper indicated that wavelet analysis can locate features of pests such as generation change and occurrence peaks.
Secondly, by using Lipschitz index, the singularity of some cycle mutation points of moth amount time series of the first generation O. furnacalis in Yantai and oviposition amount time series of the second generation H. armigera in yuncheng was analyzed, the different singular characteristics of cycle mutation points of pest time series were revealed, the sizes of singularity of the cycle mutation points were measured.
Thirdly, this paper applied wavelet decomposition to analyze and forecast the non-stationary time series of pests occurrence. The non-stationary time series was decomposed into several components with wavelet decomposition. Then, every component was analyzed and model was established. Finally, the models of all components were combined to obtain the model of the original non-stationary time series. At case study, the non-stationary time series of the first generation O. furnacalis occurrence degree in Yantai and the first generation O. furnacalis moths amount in Wendeng was used to establish forecasting model, the results showed that this method was suitable to analysis and forecast for pest non-stationary time series.
Fourthly, this paper introduced and applied radial basis wavelet network into the area of pest forecasting for the first time, modified the learning algorithms of radial basis wavelet network for application in pest forecast. At case study, the investigation data of H. armigera in Huimin, was used to establish forecasting model of oviposition peak day in the second generation of H.armigera based on radial basis wavelet network, the investigation data of H. armigera in Western shandong was used to establish forecasting model for occurrence degree of the second generation H. armigera based on radial basis wavelet network, the investigation data of O. furnacalis in Yantai was used to establish forecasting model for occurrence degree of the first generation O. furnacalis based on radial basis wavelet network. The test result showed that the forecasting result of three models was a great satisfaction.
Fifthly, based on complex wavelet transform and correlation analysis in statistic al, multi-scale correlation between pests occurrence and meteorological factors was st udied and explored preliminary. At case study, multi-scale correlation between the o viposition quantity time series of the second generation H. armigera in Hunmin and meteorological factors, multi-scale correlation between the moths quantity time serie s of the first generation O. furnacalis in Yantai and meteorological factors was anal yzed. The result showed complicated correlation existed between pests occurrence an d meteorological, wavelet analysis can analyzed this kind of complicated correlation and provide reference for forecasting models.
In summary, in this study, new theories and methods were applied to the area of crop pests forecasting and some active explorations were done to improve the ac curacy of crop pests forecasting.
本论文借助于小波分析理论的发展,首次将小波分析方法系统地引入到农作物害虫预测预报领域,利用小波分析时频局部化好的优势对害虫发生进行了精细化的分析,基于害虫发生的历史调查数据研究了如何利用小波方法分析害虫发生的特征和规律,深入探讨了小波分析如何与时间序列分析、神经网络相结合,以实例分析为基础建立了新的害虫预测模型,并对模型进行了检验。概括起来,本论文主要研究结果如下：
1、基于小波分析的方法,对害虫年际和年代际发生规律进行分析,通过对郓城县2、3、4代棉铃虫年卵量时间序列、烟台市1、2代玉米螟年诱蛾量时间序列以及济宁市、文登市2代粘虫年诱蛾量时间序列的实例分析结果表明：小波分析可以清晰的挖掘出害虫发生的年际和年代际周期,分析确定其中的主要周期,展现各演变周期中害虫数量的高低波动、分布变化以及周期突变点、周期拐点等特征；基于多分辨率分析的方法,对害虫的短期变化特征进行分析,通过临清市1995年、汶上县1994年棉铃虫逐日诱蛾量时间序列以及招远市2008年金纹细蛾逐日诱蛾量时间序列的实例分析结果表明：小波分析能够对害虫在一年中的世代更替、发生高峰等特征进行准确定位。
2、利用Lipschitz指数对害虫发生时间序列的周期突变点进行奇异性分析,通过对烟台市1代玉米螟年蛾量时间序列和郓城县二代棉铃虫年卵量时间序列中部分周期突变点的实例分析结果表明：结合小波变换所得到的Lipschitz指数能够揭示害虫发生时间序列中各周期突变点不同的奇异性特征并衡量了各突变点的奇异性大小。
3、将小波分解应用于害虫发生非平稳时间序列的分析和预测,通过小波分解,将非平稳时间序列分离为多个分量,然后分别针对各分量进行分析和建模,最后将各分量模型进行组合,从而得到原非平稳时间序列的预测模型。通过对烟台市一代玉米螟发生程度非平稳时间序列和文登市一代玉米螟蛾量非平稳时间序列进行的实例分析建模表明：小波分析适合于处理非平稳时间序列,对于那些采用传统非平稳时间序列分析难以完成建模的害虫发生非平稳时间序列,该方法能够很好的完成建模。
4、将径向基小波网络首次引入农作物害虫预测预报领域,并改进了径向基小波网络的学习算法,使之适合于害虫预测的应用。以惠民县棉铃虫的监测数据为基础建立的2代棉铃虫卵量峰值日期9维输入1维输出径向基小波网络模型、以鲁西棉区棉铃虫监测数据为基础建立的2代棉铃虫发生程度6维输入1维输出径向基小波网络模型、以德州市玉米螟监测数据为基础建立的1代玉米螟发生程度5维输入4维输出径向基小波网络模型,并分别利用5年的实况数据对上述3个模型的预测效果进行了检验,检验结果表明上述3个模型的预测效果令人满意。
5、基于cmorl-1复小波变换和统计分析的相关分析,对害虫发生与气象因素之间的多尺度相关性进行了研究和初步探索。通过对惠民县2代棉铃虫卵量数据序列和气象因子数据序列、烟台市1代玉米螟蛾量数据序列和气象因子的实例分析结果表明：害虫发生与气象因素之间存在着多尺度的复杂相关性,而采用小波分析结合统计相关的多尺度相关分析,则可以这种复杂相关,从而为分析和建立预测模型提供参考。
总之,本文运用新理论、新方法,进行了农作物害虫预测预报新方法的研究,对如何提高农作物害虫预测预报的准确率作了积极的探索。
How to improve pest forecast, especially the accuracy of long-term pest forecast, always was the core of the work and hot spots at pest forecast. The pest population dynamics showed heterogeneity, difference, diversity, and other characteristics of sudden and random because of the ecosystem complexity inherent characteristics, these characteristics led to that the accuracy of pest forecast was unsatisfactory. In recent years, thanks to development of forecasting theory and information technology, crop pest forecast technology has made great progress. Among them, it is the new direction of development of pest forecast that combining the traditional linear theory and nonlinear science including fractal, neural network, chaos theory and wavelet analysis etc, establishing the methodology of merging various disciplines, realizing the analysis and description of pests and establishing the fast and accurate prediction forecasting system. Currently, neural networks, chaos, fractal prediction have been applied in the pest forecasting, but wavelet analysis theory, as an important component of non-linear theory application has not been applied in pest forecasting.
This paper, based on development of wavelets analysis theory,-introduced wavelets analysis method into the area of pest forecasting for the first time, analyzed pest occurrence in detail with the good advantage of wavelets in time-frequency localization. This paper, studied how to use wavelets method to analyze the rules and characteristics of pest occurrence based on historical investigation data of pest, explored deeply how to combine wavelets analysis and time series analysis, neural network. This paper built the new models of pest forecasting based on instances analysis, tested the models. In summary, the major findings are as follows:
Firstly, based on wavelet analysis method, this paper analyzed inter-annual and inter-decadal rules of pests. According to the cases studying for annual oviposition quantity time series of the first, second, third generation Helicoverpa armigera in Yuncheng, annual moths quantity time series of the first, second generation Ostrinia furnacalis in Yantai, annual moths quantity time series of the second generation Mythimna separata in Jining and Wendeng, this paper indicated that wavelet analysis can dig out inter-annual and inter-decadal cycle of pests clearly, determine which of the main cycle, show the evolution and fluctuation of the cycle, cycle features such as mutation points and turnning points. On the other hand, this paper analyzed short-term rules of pests based on multi-resolution analysis. According the cases studying for daily moths quantity time series trapped by light of H. armigera at1995in Linqing and at1994in Wenshang, daily moths quantity time series trapped by light of Lithocolletis ringoniella at2008in Zhaoyuan, this paper indicated that wavelet analysis can locate features of pests such as generation change and occurrence peaks.
Secondly, by using Lipschitz index, the singularity of some cycle mutation points of moth amount time series of the first generation O. furnacalis in Yantai and oviposition amount time series of the second generation H. armigera in yuncheng was analyzed, the different singular characteristics of cycle mutation points of pest time series were revealed, the sizes of singularity of the cycle mutation points were measured.
Thirdly, this paper applied wavelet decomposition to analyze and forecast the non-stationary time series of pests occurrence. The non-stationary time series was decomposed into several components with wavelet decomposition. Then, every component was analyzed and model was established. Finally, the models of all components were combined to obtain the model of the original non-stationary time series. At case study, the non-stationary time series of the first generation O. furnacalis occurrence degree in Yantai and the first generation O. furnacalis moths amount in Wendeng was used to establish forecasting model, the results showed that this method was suitable to analysis and forecast for pest non-stationary time series.
Fourthly, this paper introduced and applied radial basis wavelet network into the area of pest forecasting for the first time, modified the learning algorithms of radial basis wavelet network for application in pest forecast. At case study, the investigation data of H. armigera in Huimin, was used to establish forecasting model of oviposition peak day in the second generation of H.armigera based on radial basis wavelet network, the investigation data of H. armigera in Western shandong was used to establish forecasting model for occurrence degree of the second generation H. armigera based on radial basis wavelet network, the investigation data of O. furnacalis in Yantai was used to establish forecasting model for occurrence degree of the first generation O. furnacalis based on radial basis wavelet network. The test result showed that the forecasting result of three models was a great satisfaction.
Fifthly, based on complex wavelet transform and correlation analysis in statistic al, multi-scale correlation between pests occurrence and meteorological factors was st udied and explored preliminary. At case study, multi-scale correlation between the o viposition quantity time series of the second generation H. armigera in Hunmin and meteorological factors, multi-scale correlation between the moths quantity time serie s of the first generation O. furnacalis in Yantai and meteorological factors was anal yzed. The result showed complicated correlation existed between pests occurrence an d meteorological, wavelet analysis can analyzed this kind of complicated correlation and provide reference for forecasting models.
In summary, in this study, new theories and methods were applied to the area of crop pests forecasting and some active explorations were done to improve the ac curacy of crop pests forecasting.
引文
1.常学将,陈敏,王明生.时间序列分析[M].北京：高等教育出版社,1993,113-196.
2. 陈生斗,胡伯海(主编).中国植物保护五十年[M].北京：中国农业出版社,2003.
3. 陈祥训.正交紧支复小波的生成及其在电力系统的应用.中国电机工程学报,2000,20(7)：83-88.
4. 陈湛云.新编统计学～描述与推断[M].上海：上海科学技术文献出版社,1994.
5.陈征,叶正襄,汪笃栋,涂努民.三化螟中长期数量预测的时间序列分析法[J].中国水稻科学,1987,1(2)：127-132.
6.崔雪梅.以复小波提取局部放电信号特征的原理及方法研究[D].重庆：重庆大学电气工程学院,2004,41.
7. 丁世飞.用多级判别技术预测害虫种群动态[J].昆虫知识,1997,34(5)：292-294.
8. 丁世飞,周法莲.数学判别模型在预测害虫种群动态上的应用[J].昆虫知识,1997,34(2)：75-77.
9.飞思科技产品开发中心.小波分析理论与MATLAB7实现[M].北京：电子工业出版社,2006.
11.韩丽萍,张文梅,尹王保.一种改进的二进小波变换图像去噪方法[J].计算机应用与软件,2006,23(4)：23-24.
12.胡伯海,姜瑞中(主编).农作物病虫长期运动规律与预测[M].北京：中国农业出版社,1997.
13.华来庆,申广荣,熊林平,孟虹,赵胜荣,胡亚萍ARIMA模型在黄瓜霜霉病疾病指数时间序列建模中的应用研究[J].第二军医大学学报,2006,27(7)：729-732.
14.贾春生ARIMA模型在马尾松毛虫发生面积预测中的应用[J].安徽农业科学,2007,35(19)：5672-5673.
15.黄荣华,叶正襄,汤建国.应用ARMA模型超长期预测棉铃虫发生程度[J].江西农业学报,1997,9(1)：40-44.
16.李百炼.昆虫种群动态预测的加权列联表分析法[J].昆虫知识,1984,21(3)：141-144.
17.李百炼.棉红蜘蛛大发生的Fuzzy数学模型初探[J].华中农业大学学报,1984,3(4)：25-32.
18.李弼程,罗建书.小波分析及其应用[M].北京：电子工业出版社,2003.
19.李力,方华京,2000.小波神经网络逼近能力及Thau定理推广.控制与决策,15(5)：561-564,568.
20.李绍石.二化螟发生趋势的马尔科夫链预测[J].植物保护,1987,13(2)：13.
21.李汝铎,丁锦华,胡国文,苏德明(主编).褐飞虱及其种群管理[M].上海：复旦大学出版社,1996.
22.林宙辰,石青云.用二进小波消除磁共振图像中的振铃效应[J].模式识别与人工智能,1999,1 2(3)：320-324.
23.刘了凡,冯殿英,张厚成.主成分分析在麦蚜发生量预报中的应用[J].昆虫知识,1997,34(2)：260-263.
24.吕雨土.褐飞虱灰色灾变长期预测初探[J].昆虫知识,1989,26(5)：257-260.
25.马社祥,刘贵忠,曾召华.基于小波分析的非平稳时间序列分析与预测[J].系统工程学报,2000,15(4)：305-311.
26.马飞,丁宗泽,沈龙元,程遐年.褐飞虱发生的一维时间序列相空间重构及混沌吸引子维数的确定[J].生态学报,2001,21(9)：1542-1548.
27.马飞,许晓风,张夕林,程遐年.神经网络预警系统及其在害虫预测中的应用[J].昆虫知识,2002,39(2)：115-119.
28.潘迪特,吴定民.时间序列及系统分析与应用[M].北京：机械出版社,1999,168-180.
29.蒲蛰龙.农作物害虫管理数学模型与应用[M].广州：广东科技出版社,1990,166-220.
30.秦前清.实用小波分析[M].西安：西安科技大学出版社,1995,1-31.
31屈西峰,姜玉英,邵振润(主编).棉铃虫预测预报新技术[M].北京：中国科学技术出版社,2000.
32任广伟,王秀芳,王新伟,李晓,张建党,梁其涛.BP神经网络在烟蚜发生程度预测中的应用.华北农学报[J],2008(增刊)：373-376.
33.苏庆玲,程极益,张孝羲.起伏型时间序列分析方法在害虫测报上的应用[J].昆虫知识,1995,32(3)：129-132.
34.苏小红,侯秋香,马培军,王亚东.RBF神经网络的混合学习算法[J].哈尔滨工业大学学报,2006,37(9)：1446-1449.
35.孙源正,原永兰(主编).山东蝗虫[M].北京：中国农业科技出版社,1999.
36.唐远炎,王玲.小波分析与文本文字识别[M].北京：科学出版社,2004.
37.田丽从.多维小波神经网络理论及在脑电逆问题中的应用[D].天津：河北工业大学计算机科学与软件学院,2000.
38.万建,徐德民,贺昱曜,2002.基于张量积结构的多维小波网络.控制理论与应用,19(3)：381-386.
39.王贺军.棉铃虫长期数量预报研究-平稳随机时间序列预报方法的应用[J].植物保护,1985,(4)：7-8.
40.王厚振,华尧楠,牟吉元(主编).棉铃虫预测预报与综合治理[M].北京：中国农业出版社,1999.
41.汪泗水,张孝羲.基于神经网络的稻纵卷叶螟长期预测.植物保护学报[J],2000,27(4)：313-316.
42.王建忠.小波理论及其在物理和工程中的应用[J].数学进展,1992,21(3)：289-316.
43.王万雄,李自珍.多种天敌控制多种害虫的模糊数学模型[J].生物数学学报,2005,20(1)：115-121.
44.王文圣,丁晶,衡彤.水文序列周期成分和突变特征识别的小波分析[J].四川大学学报(工程科学版),2003,(1)：31-35.
45.王演,李春庚,王百锁.边缘检测中的小波[J].辽宁师范大学学报(自然科学版),2003,26(03)： 244-250.
46.王振龙.时间序列分析[M].北京：中国统计出版社,1999,168-180.
47.武华文,付和平,武晓东,董维惠,徐胜利.应用时间序列分析法预测黑线仓鼠和长爪沙鼠种群数量[J].内蒙古农业大学学报,2007,28(4)：6-11.
48.吴今培,邹平.时间序列的稳健分析[J].长沙铁道学院学报,1990,8(1)：1-13.
49.吴清,沈雪勤,颜威利,2003.一种多维小波网络的构造性算法.计算机学报,26(9)：1206-1210.
50.吴耀军,陶宝祺,袁慎芳.B样条小波神经网络.模式识别与人工智能,1996,9(3)：228-233.
51.向前,林春生,龚沈光.基于小波包变换的非高斯噪声信号结构分析[J].电子与信息学报,2004,26(1)：60-64.
52.熊彩云,吴涛,习万根.灰色系统理论在植物病虫害预测中的应用[J].江西植保,2005,28(2)：49-51.
53.徐建华.现代地理学中的数学方法[M].北京,高等教育出版社,2002,419-432.
54.文新辉,陈开周.基于神经网络的多维时间序列预测预报方法.预测[J],1993,12(6)：21-24.
55.许晓风,马飞,邹运鼎,程遐年.褐飞虱发生系统的混沌诊断[J].应用生态学报,2003,14(8)：1359-1362.
56.杨福生.小波变换的工程分析与应用[M].北京：科学出版社,2000.
57.杨位钦,顾岚.时间序列分析与动态数据建模[M].北京：北京理工大学出版社,1988.
58.袁磊,李书琴.基于小波网络的小麦条锈病预测模型研究[J].人工智能,2009,25(12-2)：42-43,81.
59.张邦礼,李银国,曹长修,1995.小波神经网络的构造及其算法的鲁棒性分析.重庆大学学报(自然科学版),18(6)：88-95.
60.张大波,刘志刚,张亚军.复小波研究现状及其在电力系统中的应用进展.电力系统自动化,2006,30(17)：97-104.
61.张海松.油松毛虫发生的非线性灰色预测模型[J].昆虫知识,1993,30(1)：36-37.
62.张汝霖,于锁英,徐艳霞.高粱蚜灰色灾变预测模型[J].昆虫知识,1994,31(6)：327-329.
63.张润杰,古德祥,蒲蛰龙,周之铭,林宪德.模糊控制模型在三化螟数量预报中的应用探讨[J].生态科学,1989,(2)：10-12.
64.张善文,雷英杰,冯有前MATLAB在时间序列分析中的应用[M].西安：西安电子科技大学出版社,2007.
65.张尚志,张遵雄.三化螟中长期统计预测初步研究[J].植物保护学报,1982,9(2)：41-48.
66.张少文.黄河流域天然年径流变化特性分析及其预测[D].成都：四川大学水利水电学院,2005,20.
67.张孝羲,张跃进(主编).农作物有害生物预测学[M].北京：中国农业出版社,2005.
68.张孝羲,周立阳.害虫预测预报的理论基础[J].昆虫知识,1995,32(1)：55-60.
69.张跃进,王艳青(主编).农业病虫监测治理进展[M].北京：中国农业科技出版社,2001.
70.张遵雄,朱彭年,张尚志,涂努民,陈铿,占金庭.回归分析法在三化螟中长期预测中的应用[J].昆虫知识,1980,17(6)：246.
71.郑军,诸静.基于第二代小波变换的电力设备图像特征提取[J].电工技术学报,2003,18(6)：98-102.
72.中国植物保护学会.植物保学学科发展报告[M].北京：中国科学出版社.2008：99-112.
73.周立阳,费惠新,张孝羲.多维时间序列分析在稻纵卷叶螟长期预测上的应用[J].植物保护学报,1995,22(1)：1-6
74.周立阳,高增祥,李典谟.害虫预测预报的生态学基础和应用技术研究进展[J].生态学报,2001,21(6)：1013-1019.
75.周俊武,孙传尧,王福利.径向基函数(RBF)网络的研究及实现[J].矿冶,2001,10(4)：71-75.
76. Allen JC. Are natural enemy populations chaotic?. See Ref,1989,70:190-205.
77. Balaguer E, Palomares A, Soria E, Martin JD. Predicting service request in support centers based on nonlinear dynamics, ARMA modeling and neural networks[J]. Expert Systems with Application, 2008,34:665-672.
78. Baskshi BR, Stephanopoulous G,1993. Wave-net:a multi-resolution, hierarchical neural network with localized learning[J]. Transactions of The American Institute of Chemical Engineers Journal, 39(1):57-81.
79. Blasius B. Complex dynamics and phase synchronization in spatially extended ecological systems. Nature,1999,399:354-359.
80. Boggess A, Narcowich FJ.小波与傅立叶基础[M].芮国胜,康健,等译.北京：电子工业出版社,2004,110.
81. Box GEP, Jenkins GM, Reinsel GC.时间序列分析：预测与控制[M].顾岚译.第三版.北京：中国统计出版社,1999.
82. Brockwell PJ, Davis RA. Time series:Theory and Methods(the second edition) [M]. New York: Spring-Verlag,1991:250-253.
83. Cahill AT. Determination of changes in streamflow variance by means of a wavelet-based test. Water Resources Research,2002,38(6):1-14.
84. Chou JJ, Chen CP, Joannie TY. Crop identification with wavelet packet analysis and weighted Bayesian distance[J]. Computers and Electronics in Agriculture,2007,57:88-98.
85. Daubechies I. Where do wavelet come from:a personal point of view[J]. Proceedings of IEEE,1986, 84(4):510-513.
86. Daubechies I.小波十讲(M).李建平,杨万年译.北京：国防工业出版社,2004.
87. Duffin RJ, Schaeffer AC. A class of nonharmonic Fourier series[J]. Trans. Amer Math. Soc.,1952, 72(2):341-366.
88. Delyon B, Juditsky A, Benveniste A. Accuracy analysis for wavelet approximations[J]. IEEE Trans on Neural Network,1995,6(2):332-348.
89. Goswami JC, Chan AK.小波分析理论、算法及其应用[M].许天周,黄春光译.北京：国防工业 出版社,2007.
90. Jaffard S, Meyer Y, Ryan RD.科学技术中的小波分析[M].李建平,等译.北京：国防工业出版社,2006.
91. Kitagawa G, Gerch W. A smoothness priors-state space modeling of time series with trend seasonality[J], Jounal of American Statistical Association,1984,3:378-381.
92. Kizilkaya A, Kayran A H. ARMA model parameter estimation based on the equivalent[J]. Digital Signal Processing,2006,16:670-681.
93. Kugarajah T, Zhang Q. Multidimensional wavelet frames[J]. IEEE Transactions on Neural Networks, 1995,6(6):1552-1556.
94. Li Y, Xie ZJ. The wavelet detection of hidden periodicities in time series[J]. Statistics & Probability Letters,1997,35(1):9-23.
95. Logan JA, Allen JC. Nonlinear dynamics and chaos in insect populations. Ann. Rev. Entomol,1992, 37:455-477.
96. Mallat S G Multiresolution representation and wavelets[D]. PhD Thesis, University Of Pen-nsylvania, Philadelphia,1988.
97. Mallat S G A theory of multiresolution signal decomposition:The wavelet representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1989,11(7):674-693.
98. Mallat S G. Multifrequency channel decompositions of images and wavelet models[J]. IEEE Transactions on Acoustics, Speech Signal Processing,1989,37(12):2091-2110.
99. Mallat S G. Multiresolution approximations and wavelet orthonormal bases of L2(R)[J].Trans.Amer. Math. Soc.,1989,315(1):69-87.
100. Mallat S G信号处理的小波导引[M].杨力华,戴道清,黄文良,等译.北京：机械工业出版社,2002.
101.Masanao A. State Space Modeling of Time Series[M]. Springer-Verlag Berlin Heidelberg Germany, 1987:287-298.
102.May R M. Simple mathematical models with very complicated dynamics. Nature,1976,261: 459-467.
103.Meyer Y. Wavelets and Operators (Cambridge Studies in Advanced Mathematics 37)[M]. Cambridge:Cambridge University Press,1992.
104.Meyer Y. Wavelets-algorithms and applications[M]. Philadelphia:SIAM,1993,1-340.
105.Ott R L, Longnecker M.统计学方法与数据分析引论[M].张忠占,等译.北京：科学出版社,2003,649-659.
106.Pati Y C, Krishnaprasad PS. Analysis and synthesis of feedforward neural networks using discrete affine wavelet transformations. IEEE Transaction on Neural Network,1993,4(1):73-75.
107.Philippe A. Bayesian analysis of autogressive moving average processes with unknown orders[J]. Computational Statistics & Data Analysis,2006,51:1904-1923.
108.Shumway R H. Applied Statistical Time Series Analysis. Englewood Clifs, NJ:Prentice-Hall,1988: 210-320.
109.Si B C, Richard E F. Scale-Dependent relationship between wheat yield and topographic indices:a wavelet approach[J]. Soil Science Society of America Journal,2004,68(2):577-587.
110.Zhang J, Walter G, Miao Y, Lee W. Wavelet neural networks for function learning[J]. IEEE Transaction on Signal Processing,1995,43(6):1485-1496.
111.Zhang QH, Benveniste A. Wavelet networks. IEEE Transactions on Neural Networks,1992,3(6): 889-898.
112.Zhang QH. Using wavelet networks in nonparametric estimation[J]. IEEE Transactions on Neural Networks,1997,8(2):227-236.
2. 陈生斗,胡伯海(主编).中国植物保护五十年[M].北京：中国农业出版社,2003.
3. 陈祥训.正交紧支复小波的生成及其在电力系统的应用.中国电机工程学报,2000,20(7)：83-88.
4. 陈湛云.新编统计学～描述与推断[M].上海：上海科学技术文献出版社,1994.
5.陈征,叶正襄,汪笃栋,涂努民.三化螟中长期数量预测的时间序列分析法[J].中国水稻科学,1987,1(2)：127-132.
6.崔雪梅.以复小波提取局部放电信号特征的原理及方法研究[D].重庆：重庆大学电气工程学院,2004,41.
7. 丁世飞.用多级判别技术预测害虫种群动态[J].昆虫知识,1997,34(5)：292-294.
8. 丁世飞,周法莲.数学判别模型在预测害虫种群动态上的应用[J].昆虫知识,1997,34(2)：75-77.
9.飞思科技产品开发中心.小波分析理论与MATLAB7实现[M].北京：电子工业出版社,2006.
11.韩丽萍,张文梅,尹王保.一种改进的二进小波变换图像去噪方法[J].计算机应用与软件,2006,23(4)：23-24.
12.胡伯海,姜瑞中(主编).农作物病虫长期运动规律与预测[M].北京：中国农业出版社,1997.
13.华来庆,申广荣,熊林平,孟虹,赵胜荣,胡亚萍ARIMA模型在黄瓜霜霉病疾病指数时间序列建模中的应用研究[J].第二军医大学学报,2006,27(7)：729-732.
14.贾春生ARIMA模型在马尾松毛虫发生面积预测中的应用[J].安徽农业科学,2007,35(19)：5672-5673.
15.黄荣华,叶正襄,汤建国.应用ARMA模型超长期预测棉铃虫发生程度[J].江西农业学报,1997,9(1)：40-44.
16.李百炼.昆虫种群动态预测的加权列联表分析法[J].昆虫知识,1984,21(3)：141-144.
17.李百炼.棉红蜘蛛大发生的Fuzzy数学模型初探[J].华中农业大学学报,1984,3(4)：25-32.
18.李弼程,罗建书.小波分析及其应用[M].北京：电子工业出版社,2003.
19.李力,方华京,2000.小波神经网络逼近能力及Thau定理推广.控制与决策,15(5)：561-564,568.
20.李绍石.二化螟发生趋势的马尔科夫链预测[J].植物保护,1987,13(2)：13.
21.李汝铎,丁锦华,胡国文,苏德明(主编).褐飞虱及其种群管理[M].上海：复旦大学出版社,1996.
22.林宙辰,石青云.用二进小波消除磁共振图像中的振铃效应[J].模式识别与人工智能,1999,1 2(3)：320-324.
23.刘了凡,冯殿英,张厚成.主成分分析在麦蚜发生量预报中的应用[J].昆虫知识,1997,34(2)：260-263.
24.吕雨土.褐飞虱灰色灾变长期预测初探[J].昆虫知识,1989,26(5)：257-260.
25.马社祥,刘贵忠,曾召华.基于小波分析的非平稳时间序列分析与预测[J].系统工程学报,2000,15(4)：305-311.
26.马飞,丁宗泽,沈龙元,程遐年.褐飞虱发生的一维时间序列相空间重构及混沌吸引子维数的确定[J].生态学报,2001,21(9)：1542-1548.
27.马飞,许晓风,张夕林,程遐年.神经网络预警系统及其在害虫预测中的应用[J].昆虫知识,2002,39(2)：115-119.
28.潘迪特,吴定民.时间序列及系统分析与应用[M].北京：机械出版社,1999,168-180.
29.蒲蛰龙.农作物害虫管理数学模型与应用[M].广州：广东科技出版社,1990,166-220.
30.秦前清.实用小波分析[M].西安：西安科技大学出版社,1995,1-31.
31屈西峰,姜玉英,邵振润(主编).棉铃虫预测预报新技术[M].北京：中国科学技术出版社,2000.
32任广伟,王秀芳,王新伟,李晓,张建党,梁其涛.BP神经网络在烟蚜发生程度预测中的应用.华北农学报[J],2008(增刊)：373-376.
33.苏庆玲,程极益,张孝羲.起伏型时间序列分析方法在害虫测报上的应用[J].昆虫知识,1995,32(3)：129-132.
34.苏小红,侯秋香,马培军,王亚东.RBF神经网络的混合学习算法[J].哈尔滨工业大学学报,2006,37(9)：1446-1449.
35.孙源正,原永兰(主编).山东蝗虫[M].北京：中国农业科技出版社,1999.
36.唐远炎,王玲.小波分析与文本文字识别[M].北京：科学出版社,2004.
37.田丽从.多维小波神经网络理论及在脑电逆问题中的应用[D].天津：河北工业大学计算机科学与软件学院,2000.
38.万建,徐德民,贺昱曜,2002.基于张量积结构的多维小波网络.控制理论与应用,19(3)：381-386.
39.王贺军.棉铃虫长期数量预报研究-平稳随机时间序列预报方法的应用[J].植物保护,1985,(4)：7-8.
40.王厚振,华尧楠,牟吉元(主编).棉铃虫预测预报与综合治理[M].北京：中国农业出版社,1999.
41.汪泗水,张孝羲.基于神经网络的稻纵卷叶螟长期预测.植物保护学报[J],2000,27(4)：313-316.
42.王建忠.小波理论及其在物理和工程中的应用[J].数学进展,1992,21(3)：289-316.
43.王万雄,李自珍.多种天敌控制多种害虫的模糊数学模型[J].生物数学学报,2005,20(1)：115-121.
44.王文圣,丁晶,衡彤.水文序列周期成分和突变特征识别的小波分析[J].四川大学学报(工程科学版),2003,(1)：31-35.
45.王演,李春庚,王百锁.边缘检测中的小波[J].辽宁师范大学学报(自然科学版),2003,26(03)： 244-250.
46.王振龙.时间序列分析[M].北京：中国统计出版社,1999,168-180.
47.武华文,付和平,武晓东,董维惠,徐胜利.应用时间序列分析法预测黑线仓鼠和长爪沙鼠种群数量[J].内蒙古农业大学学报,2007,28(4)：6-11.
48.吴今培,邹平.时间序列的稳健分析[J].长沙铁道学院学报,1990,8(1)：1-13.
49.吴清,沈雪勤,颜威利,2003.一种多维小波网络的构造性算法.计算机学报,26(9)：1206-1210.
50.吴耀军,陶宝祺,袁慎芳.B样条小波神经网络.模式识别与人工智能,1996,9(3)：228-233.
51.向前,林春生,龚沈光.基于小波包变换的非高斯噪声信号结构分析[J].电子与信息学报,2004,26(1)：60-64.
52.熊彩云,吴涛,习万根.灰色系统理论在植物病虫害预测中的应用[J].江西植保,2005,28(2)：49-51.
53.徐建华.现代地理学中的数学方法[M].北京,高等教育出版社,2002,419-432.
54.文新辉,陈开周.基于神经网络的多维时间序列预测预报方法.预测[J],1993,12(6)：21-24.
55.许晓风,马飞,邹运鼎,程遐年.褐飞虱发生系统的混沌诊断[J].应用生态学报,2003,14(8)：1359-1362.
56.杨福生.小波变换的工程分析与应用[M].北京：科学出版社,2000.
57.杨位钦,顾岚.时间序列分析与动态数据建模[M].北京：北京理工大学出版社,1988.
58.袁磊,李书琴.基于小波网络的小麦条锈病预测模型研究[J].人工智能,2009,25(12-2)：42-43,81.
59.张邦礼,李银国,曹长修,1995.小波神经网络的构造及其算法的鲁棒性分析.重庆大学学报(自然科学版),18(6)：88-95.
60.张大波,刘志刚,张亚军.复小波研究现状及其在电力系统中的应用进展.电力系统自动化,2006,30(17)：97-104.
61.张海松.油松毛虫发生的非线性灰色预测模型[J].昆虫知识,1993,30(1)：36-37.
62.张汝霖,于锁英,徐艳霞.高粱蚜灰色灾变预测模型[J].昆虫知识,1994,31(6)：327-329.
63.张润杰,古德祥,蒲蛰龙,周之铭,林宪德.模糊控制模型在三化螟数量预报中的应用探讨[J].生态科学,1989,(2)：10-12.
64.张善文,雷英杰,冯有前MATLAB在时间序列分析中的应用[M].西安：西安电子科技大学出版社,2007.
65.张尚志,张遵雄.三化螟中长期统计预测初步研究[J].植物保护学报,1982,9(2)：41-48.
66.张少文.黄河流域天然年径流变化特性分析及其预测[D].成都：四川大学水利水电学院,2005,20.
67.张孝羲,张跃进(主编).农作物有害生物预测学[M].北京：中国农业出版社,2005.
68.张孝羲,周立阳.害虫预测预报的理论基础[J].昆虫知识,1995,32(1)：55-60.
69.张跃进,王艳青(主编).农业病虫监测治理进展[M].北京：中国农业科技出版社,2001.
70.张遵雄,朱彭年,张尚志,涂努民,陈铿,占金庭.回归分析法在三化螟中长期预测中的应用[J].昆虫知识,1980,17(6)：246.
71.郑军,诸静.基于第二代小波变换的电力设备图像特征提取[J].电工技术学报,2003,18(6)：98-102.
72.中国植物保护学会.植物保学学科发展报告[M].北京：中国科学出版社.2008：99-112.
73.周立阳,费惠新,张孝羲.多维时间序列分析在稻纵卷叶螟长期预测上的应用[J].植物保护学报,1995,22(1)：1-6
74.周立阳,高增祥,李典谟.害虫预测预报的生态学基础和应用技术研究进展[J].生态学报,2001,21(6)：1013-1019.
75.周俊武,孙传尧,王福利.径向基函数(RBF)网络的研究及实现[J].矿冶,2001,10(4)：71-75.
76. Allen JC. Are natural enemy populations chaotic?. See Ref,1989,70:190-205.
77. Balaguer E, Palomares A, Soria E, Martin JD. Predicting service request in support centers based on nonlinear dynamics, ARMA modeling and neural networks[J]. Expert Systems with Application, 2008,34:665-672.
78. Baskshi BR, Stephanopoulous G,1993. Wave-net:a multi-resolution, hierarchical neural network with localized learning[J]. Transactions of The American Institute of Chemical Engineers Journal, 39(1):57-81.
79. Blasius B. Complex dynamics and phase synchronization in spatially extended ecological systems. Nature,1999,399:354-359.
80. Boggess A, Narcowich FJ.小波与傅立叶基础[M].芮国胜,康健,等译.北京：电子工业出版社,2004,110.
81. Box GEP, Jenkins GM, Reinsel GC.时间序列分析：预测与控制[M].顾岚译.第三版.北京：中国统计出版社,1999.
82. Brockwell PJ, Davis RA. Time series:Theory and Methods(the second edition) [M]. New York: Spring-Verlag,1991:250-253.
83. Cahill AT. Determination of changes in streamflow variance by means of a wavelet-based test. Water Resources Research,2002,38(6):1-14.
84. Chou JJ, Chen CP, Joannie TY. Crop identification with wavelet packet analysis and weighted Bayesian distance[J]. Computers and Electronics in Agriculture,2007,57:88-98.
85. Daubechies I. Where do wavelet come from:a personal point of view[J]. Proceedings of IEEE,1986, 84(4):510-513.
86. Daubechies I.小波十讲(M).李建平,杨万年译.北京：国防工业出版社,2004.
87. Duffin RJ, Schaeffer AC. A class of nonharmonic Fourier series[J]. Trans. Amer Math. Soc.,1952, 72(2):341-366.
88. Delyon B, Juditsky A, Benveniste A. Accuracy analysis for wavelet approximations[J]. IEEE Trans on Neural Network,1995,6(2):332-348.
89. Goswami JC, Chan AK.小波分析理论、算法及其应用[M].许天周,黄春光译.北京：国防工业 出版社,2007.
90. Jaffard S, Meyer Y, Ryan RD.科学技术中的小波分析[M].李建平,等译.北京：国防工业出版社,2006.
91. Kitagawa G, Gerch W. A smoothness priors-state space modeling of time series with trend seasonality[J], Jounal of American Statistical Association,1984,3:378-381.
92. Kizilkaya A, Kayran A H. ARMA model parameter estimation based on the equivalent[J]. Digital Signal Processing,2006,16:670-681.
93. Kugarajah T, Zhang Q. Multidimensional wavelet frames[J]. IEEE Transactions on Neural Networks, 1995,6(6):1552-1556.
94. Li Y, Xie ZJ. The wavelet detection of hidden periodicities in time series[J]. Statistics & Probability Letters,1997,35(1):9-23.
95. Logan JA, Allen JC. Nonlinear dynamics and chaos in insect populations. Ann. Rev. Entomol,1992, 37:455-477.
96. Mallat S G Multiresolution representation and wavelets[D]. PhD Thesis, University Of Pen-nsylvania, Philadelphia,1988.
97. Mallat S G A theory of multiresolution signal decomposition:The wavelet representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1989,11(7):674-693.
98. Mallat S G. Multifrequency channel decompositions of images and wavelet models[J]. IEEE Transactions on Acoustics, Speech Signal Processing,1989,37(12):2091-2110.
99. Mallat S G. Multiresolution approximations and wavelet orthonormal bases of L2(R)[J].Trans.Amer. Math. Soc.,1989,315(1):69-87.
100. Mallat S G信号处理的小波导引[M].杨力华,戴道清,黄文良,等译.北京：机械工业出版社,2002.
101.Masanao A. State Space Modeling of Time Series[M]. Springer-Verlag Berlin Heidelberg Germany, 1987:287-298.
102.May R M. Simple mathematical models with very complicated dynamics. Nature,1976,261: 459-467.
103.Meyer Y. Wavelets and Operators (Cambridge Studies in Advanced Mathematics 37)[M]. Cambridge:Cambridge University Press,1992.
104.Meyer Y. Wavelets-algorithms and applications[M]. Philadelphia:SIAM,1993,1-340.
105.Ott R L, Longnecker M.统计学方法与数据分析引论[M].张忠占,等译.北京：科学出版社,2003,649-659.
106.Pati Y C, Krishnaprasad PS. Analysis and synthesis of feedforward neural networks using discrete affine wavelet transformations. IEEE Transaction on Neural Network,1993,4(1):73-75.
107.Philippe A. Bayesian analysis of autogressive moving average processes with unknown orders[J]. Computational Statistics & Data Analysis,2006,51:1904-1923.
108.Shumway R H. Applied Statistical Time Series Analysis. Englewood Clifs, NJ:Prentice-Hall,1988: 210-320.
109.Si B C, Richard E F. Scale-Dependent relationship between wheat yield and topographic indices:a wavelet approach[J]. Soil Science Society of America Journal,2004,68(2):577-587.
110.Zhang J, Walter G, Miao Y, Lee W. Wavelet neural networks for function learning[J]. IEEE Transaction on Signal Processing,1995,43(6):1485-1496.
111.Zhang QH, Benveniste A. Wavelet networks. IEEE Transactions on Neural Networks,1992,3(6): 889-898.
112.Zhang QH. Using wavelet networks in nonparametric estimation[J]. IEEE Transactions on Neural Networks,1997,8(2):227-236.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |