基于时延神经网络的木材干燥模型辨识的研究

木材干燥是一个复杂、多变的过程，传统的系统辨识方法很难建立一个理想的木材干燥模型。利用时延神经网络的特点，本文提出了基于时延神经网络的木材干燥模型辨识方法，并给出了辨识网络和结构。对辨识得到模型的仿真结果表明，利用时延神经网络所建立的模型是可靠且有效的。     

中国木工机械制造业的国际竞争力探讨

首先阐述了我国木工机械制造业的发展现状,然后运用迈克尔.波特的钻石模型对我国木工机械制造业国际竞争力的影响因素进行了深入分析,最后提出提升我国木工机械制造业国际竞争力的几点建议。     

荒漠化程度评价高光谱遥感信息模型

本文用国产高光谱分辨率成像光谱仪系统数据对荒漠化评价建立定量化遥感信息模型。对荒漠化评价因子中的主要定量因子 (植被盖度、生物量和土壤含水率 )进行了定量反演 ;对难于进行定量计算的评价指标 ,先通过目视解译获得各因子的编码图 ,分别进行影像化后参加荒漠化程度评价遥感信息模型计算。通过每个像元都可获取全部评价因子的指标值 ,在现有的荒漠化评价方法的基础上 ,建立以像元为单位的荒漠化程度评价的定量化遥感信息模型并输出荒漠化程度分布图。结果表明 ,用高光谱数据定量反演荒漠化地区植被生物量、盖度和土壤含水率是比较可靠的。当反演区域内灌木和草地同时存在时多项式模型的精度要明显高于线性模型 ;当植被类型单一时 ,模型即为较高精度的线性模型 ,但模型的应用地域范围受到限制 ,只能分块进行计算。因此 ,在只有灌木和草地的区域用多项式模型反演会提高效率。土壤含水量的反演方法适合于地形平坦、植被比较稀疏的条件。但研究发现 ,基于土壤热惯量的含水量模型具有一定的抗植被干扰能力。荒漠化程度评价的遥感信息模型的精度主要取决于现有荒漠化评价的方法 (即评价指标是否科学合理、专家给定的权重和等级标准是否客观 )以及各指标数据的获取精度     

Growth and yield prediction models for Acacia mangium grown in the plantations of the central region of Bangladesh

Acacia mangium is a very fast growing species belonging to the family Leguminosae that has been introduced in the plantations in Bangladesh for its faster growth and wide range of adaptability. The present study aimed at development of growth and yield prediction models for the species using simultaneous equation method. Models were selected for the species to estimate stand dominant height, stand diameter, stand basal area per hectare and total volume yield per hectare. Paired t-test, 45-degree line test, percent absolute deviation and biological principle of stand development were used for the validation of chosen models. The results suggest that the models derived were statistically and biologically acceptable and could be satisfactorily used for stands of Acacia mangium of ages 4–7 yrs based on a base age of 6 yr.     

贮木场选址模型的研究

建立了一个贮木场选址的数学模型，这个模型将动态经济评价方法，运输网络和贮木场选址有机地结合起来。     

王朗林区人工缺苞箭竹亚层数量特征及其模型

王朗林区人工缺苞箭竹亚层数量特征及其模型刘兴良，向性明（四川省林业科学研究院成都６１００８１）关键词四川王朗，亚高山暗针叶林，亚建群层，缺苞箭竹，大熊猫，仿真造林缺苞箭竹（Ｆａｒｇｅｓｉａｄｅｎｕｄａｔａ）是四川王朗林区亚高山暗针叶林下亚建群层的主要...     

深圳城市公园植物典型配置模式初探

深圳城市公园生境多样,景观类型丰富,造景水平较高;通过对深圳城市公园植物配置实用模式的调查研究,分析了应用植物种类的选择,总结提炼了18种典型的景观单元配置模式,并提出有利于今后发展的相关工作建议。     

Comparing the model forms estimating generalised diameter-height relationships in Tecomella undulata plantations in hot arid region of India

Four generalised diameter-height equations were developed and compared for pure and even-aged stands of Tecomella undulata in hot arid region of Rajasthan State in India. The data used to fit the equations consisted of 1 540 diameter-height observations collected from the plots laid out in uniformly stocked stands of varying age and density. The performance of four equations was tested by non-linear least squares regression and evaluated using different statistical criteria. Finally, these equations, with the same values of coefficients ob- tained during the fitting phase, were validated by an independent data set consisting of 854 diameter-height observations. Overall, equation （4） （Hui and Gadow function） was found to perform best for both the fitting data set as well as validation data set.     

红松高生长模型的研究(英文)

高生长模型是定量研究树木生长过程的有效途径,本文应用双曲线方程、Logistic方程、三个参数的Richards方程及四个参数的Richards方程对红松的高生长进行了研究。结果表明四个参数的Richards方程在模拟红松高生长时最为合适,且当其参数取不同的数值时,该方程可转化为其它理论方程。在使用四个参数的Richards方程时,可根据立地条件事先给定树高最大值,这样可使模型更符合实际情况。此外,文中还对一种以固定年龄时实际树高为参数值的模型进行了讨论,该模型对于计算给定树木的高生长将更为有效。     

N tracing models with a Monte Carlo optimization procedure provide new insights on gross N transformations in soils

¹⁵N tracing studies in combination with analyses via process-based models are the current “state-of-the-art” technique to quantify gross nitrogen (N) transformation rates in soils. A crucial component of this technique is the optimization algorithm which primarily decides how many model parameters can simultaneously be estimated. Recently, we published a Markov chain Monte Carlo (MCMC) method which has the potential to simultaneously estimate large number of parameters in ¹⁵N tracing models [Müller et al., 2007. Estimation of parameters in complex ¹⁵N tracing models by Monte Carlo sampling. Soil Biology & Biochemistry 39, 715-726].Here, we present the results of a reanalysis of datasets by Kirkham and Bartholomew [1954. Equations for following nutrient transformations in soil, utilizing tracer data. Soil Science Society of America Proceedings 18, 33-34], Myrold and Tiedje [1986. Simultaneous estimation of several nitrogen cycle rates using ¹⁵N: theory and application. Soil Biology & Biochemistry 18, 559-568] and Watson et al. [2000. Overestimation of gross N transformation rates in grassland soils due to non-uniform exploitation of applied and native pools. Soil Biology & Biochemistry 32, 2019-2030] using the MCMC technique. Analytical solutions such as the ones derived by Kirkham and Bartholomew [1954. Equations for following nutrient transformations in soil, utilizing tracer data. Soil Science Society of America Proceedings 18, 33-34] result in gross rates without uncertainties. We show that the analysis of the same data sets with the MCMC method provides standard deviations for gross N transformations. The standard deviations are further reduced if realistic data uncertainties are considered. Reanalyzing data by Myrold and Tiedje [1986. Simultaneous estimation of several nitrogen cycle rates using ¹⁵N: theory and application. Soil Biology & Biochemistry 18, 559-568] (Capac soil) resulted in a model fit similar to the one of the original analysis but with more precise estimates of gross N transformations. In addition, our analysis showed that small N transformations such as heterotrophic nitrification, which was neglected in the original analysis, could be quantified for this soil. Watson et al. [2000. Overestimation of gross N transformation rates in grassland soils due to non-uniform exploitation of applied and native pools. Soil Biology & Biochemistry 32, 2019-2030] provided evidence of a non-uniform exploitation of applied and native N that led to an overestimation of gross N transformations. Reanalyzing the data (CENIT soil, low N application) with the Müller et al. [2007. Estimation of parameters in complex ¹⁵N tracing models by Monte Carlo sampling. Soil Biology & Biochemistry 39, 715-726] model where oxidation was set to Michaelis-Menten kinetics resulted in a satisfactory fit between modeled and observed data, indicating that the observed artifact by Watson et al. [2000. Overestimation of gross N transformation rates in grassland soils due to non-uniform exploitation of applied and native pools. Soil Biology & Biochemistry 32, 2019-2030] was mainly due to inappropriate kinetic settings. Our study shows that the combination of a MCMC method with ¹⁵N tracing models is able to consider more complex and possibly more realistic models and kinetic settings to estimate gross N transformation rates and thus overcomes restriction of previous ¹⁵N tracing techniques.