[1] 李坚, 郭明辉, 赵西平. 木材品质与营林环境[M]. 北京:科学出版社, 2011.
[2] Joel W Evans, John F Senft, David W Green. Juvenile wood effect in red alder:Analysis of physical and mechanical data to delineate juvenile and mature wood zones [J]. Forest Products Journal, 2000, 50(7/8):75-87.
[3] Jérome Alteyrac, Alain Cloutier, Zhang S Y. Characterization of juvenile wood to mature wood transition age in black spruce(Picea mariana(Mill.)B.S.P.)at different stand densities and sampling heights [J]. Wood Science and Technology, 2006,40(2):124-138.
[4] Rüdiger Mutz, Edith Guilley, Udo H Sauter, et al. Modeling juvenile-mature wood transition in Scots pine(Pinus sylvestris L.)using nonlinear mixed-effects models [J]. Annals of Forest Science, 2004,61(8):831-841.
[5] Ferreira A L, Severo E T D, Calonego F W. Determination of fiber length and juvenile and mature wood zones from Hevea brasiliensis trees grown in Brazil [J]. Eur J Wood Prod, 2011,69(4):659-662.
[6] Ryouta Tsuchiya. Relationship between the radial variation of ray characteristics and the stages of radial stem increment in Zelkova serrata [J]. The Japan Wood Research Society, 2010,56(6):495-501.
[7] Jianjun Zhu, Naoki Tadooka, Katsuhiko Takata, et al. Growth and wood quality of sugi (Cryptomeria japonica) planted in Akita prefecture(II)[J]. Juvenile/mature Wood Determination of Aged Trees, 2005,51(2):95-101.
[8] Chih-Ming Chiu, Song-Yung Wang, Cheng-Jung Lin, et al. Application of the fractometer for crushing strength:juvenile-mature wood demarcation in Taiwania(Taiwania cryptomerioids)[J]. The Japan Wood Research Society, 2006(52):9-14.
[9] Levente Csoka, Jianjun Zhu, Katsuhiko Takata. Application of the fourier analysis to determine the demarcation between juvenile and mature wood [J]. The Japan Wood Research Society, 2005(51):309-311.
[10] 王金满, 刘一星, 李坚. 人工林长白落叶松木材材质早期预测模式(Ⅰ)——材性变异、幼龄期与成熟期的界定[J]. 东北林业大学学报, 1996, 24(5):65-71. Wang J M, Liu Y X, Li J. Early prediction model on wood properties of Larix olgensis A. plantation(Ⅰ)——Variation on wood properties, demarcation of juvenile and mature period[J]. Journal of Northeast Forestry University, 1996, 24(5):65-71.
[11] 陈广胜. 基于神经网络的人工林落叶松木材材质预测研究[D]. 哈尔滨:东北林业大学, 2006.
[12] 钟伟华, 何昭珩, 周达, 等. 主成分聚类在火炬松种子园疏伐中的运用[J]. 福建林学院学报, 1999, 19(1):33-36. Zhong W H, He S H, Zhou D, et al. The application of principal component analysis on thinning of loblolly pine seed orchard[J]. Journal of Fujian College of Forestry, 1999, 19(1):33-36.
[13] 陈建宏, 蒋权, 郑海力, 等. 基于主成分聚类分析的采矿方法优选[J]. 科技导报, 2011, 29(27):36-41. Chen J H, Jiang Q, Zheng H L, et al. Optimization of mining methods based on principal composition cluster analysis[J]. Science & Technology Review, 2011,29(27):36-41.
[14] Gaitani N. Lehmann C, Santamouris M. et al. Using principal component and cluster analysis in the heating evaluation of the school building sector [J]. Applied Energy, 2010(87):2079-2086.
[15] Gaudreault N, Mezghani N, Fuentes A, et al. Principal component clustering of frontal plane knee kinematics [J]. Osteoarthritis and Cartilage, 2010,18:S17.
[16] 马逢时, 吴诚欧, 菜霞. 基于MINITAB的现代实用统计[M]. 北京:中国人民大学出版社, 2009.
[17] 费本华, 郭伟, 张赛男, 等. 人工神经网络技术在木材科学中的应用[J]. 木材加工机械, 2009(3):34-37. Fei B H, Guo W, Zhang S N, et al. Artificial neural network technology in the application of wood science[J]. Wood Processing Machinery, 2009(3):34-37.
[18] 韩力群.人工神经网络教程[M]. 北京:北京邮电大学出版社, 2006.
[19] 史峰. 王小川. 郁磊. 等. 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.
[20] Zhu X D, Cao J, Wang F H, et al. Wood defect identification based on artificial neural network [J]. Communications in Computer and Information Science, 2009, 51(4):207-214.
[21] Wang Lihai, Qi Wei, Li L. et al. Pattern recognition and size determination of internal wood defects based on wavelet neural networks [J]. Computer and Electronics in Agriculture, 2009(69):142-148.
[22] Marco Castellani, Hefin Rowlands. Evolutionary artificial neural network design and training for wood veneer classification [J]. Engineering Applications of Artificial Intelligence, 2009(22):732-741.
[23] 江泽慧. 姜笑梅. 木材结构与其品质特性的相关性[M]. 北京:科学出版社, 2008.
[24] Zhang D Y, Sun L P, Cao J. Modeling of temperature-humidity for wood drying based on time-delay neural network [J]. Journal of Forestry Research, 2006, 17(2):141-144.
[25] 陈永义,俞小鼎,高学浩,等. 处理非线性分类和回归问题的一种新方法(Ⅰ)——支持向量机方法简介[J]. 应用气象学报, 2004, 15(3):355-365. Chen Y Y, Yu X D, Gao X H, et al. A new method for non-linear classify and non-linear regression:introduction to support vector machine[J]. Quarterly Journal of Applied Meteorology, 2004,15(3):355-365.
[26] 付阳. 李昆仑. 支持向量机模型参数选择方法综述[J]. 电脑知识与技术, 2010, 6(28):8081-8082. Fu Y, Li K L. A survey of model parameters selection method for support vector machines[J]. Computer Knowledge and Technology, 2010, 6(28):8081-8082.
[27] 杨少春. 木材表面颜色模式识别方法的研究[D]. 哈尔滨:东北林业大学, 2008. Yang S C. Research on pattern recognition of wood surface color[D]. Harbin: Northeast Forestry University, 2008.
[28] 业宁,王厚立,徐兆军,等. 基于支持向量机的木材缺陷识别[J]. 计算机应用与软件, 2006, 23(4):3-5. Ye N, Wang H L, Xu Z J, et al. Recognition of wood defection based on support vector machine[J]. Computer Applications and Software, 2006, 23(4):3-5.
[29] Irene Y H, Gu Henrik Andersson, Raúl Vicen. Automatic classification of wood defects using support vector machines[J]. Lecture Notes in Computer Science, 2009,5337:356-367.
[30] Irene Yu-Hua Gu, Henrik Andersson, Raul Vicen. Wood defect classification based on image analysis and support vector machines[J]. Wood Science and Technology, 2010,44(4):693-704.
[31] 陈立生. 基于支持向量机的木材干燥预测控制技术[D].哈尔滨:东北林业大学, 2011. Chen L S. Timber drying model predictive control technology based on support vector machine[D]. Harbin: Doctor of Northeast Forestry University, 2011.