基于随机森林算法和气象因子的砀山酥梨始花期预报

准确预报始花期是制定砀山酥梨花期管理措施和赏花活动方案的重要基础。该文利用1983-2018年砀山酥梨始花期的定位观测物候数据和平行观测的气象资料，采用线性趋势法，揭示始花期演变趋势；采用相关分析，筛选影响始花期的关键气象因子，依据不同预报日期构成特征变量集；采用随机森林算法（Random Forest, RF），自3月11日开始预报到3月25日终止预报，每日训练1个预报模型。结果表明，1）1983－2018年始花期呈极显著提早发生趋势，每10a约提前2.750 d（P＜0.001）。2）16个逐日气象预报模型中，共计有200个气象因子与始花期早迟密切相关，相关系数在0.469～0.789之间；各气象预报模型的训练集与测试集的平均正确率（Nd）分别为92.9%和75.5%、平均均方根误差（RMSE）分别为1.693～2.870和2.240～7.237、平均决定系数（R2）分别为0.891和0.701。3）2019年试验预报中，提前15日准确预报出当年始花期。该文研究表明RF在梨树始花期逐日气象预报中有一定业务应用潜力，预报准确率基本满足气象服务需求。     

基于模糊神经网络的智能温室环境控制方案

针对农业温室环境的精确建模和控制问题,提出了一种基于模糊神经网络的智能控制方案。首先,在考虑室内外环境因素下,构建一个有效的温室环境数学模型,获得通风量、喷雾量和加热量的微分表达式;然后,利用一种自适应模糊神经推理系统(ANFIS),以温度和湿度差作为输入,通过神经网络自学习和模糊推理获得控制输出;最后,通过遗传算法优化控制器的输出比例因子,提高控制响应速度和稳定性。实验结果表明:该方案能够快速且稳定地追踪环境设置值,具有很好的控制效果。     

New Approaches to Modeling Methyl Jasmonate Effects on Pomegranate Quality during Postharvest Storage

Adaptive neuro fuzzy inference system (ANFIS) and genetic algorithm–artificial neural network (GA-ANN) models were used to predict the effect of methyl jasmonate (at three levels 0, 0.01, and 0.1 mM) and storage time (0, 14, 28, 42, 56, 70, and 84 days) on quality parameters and physiological changes of pomegranate fruits during storage. Methyl jasmonate reduced chilling injury and improved quality characteristics of pomegranates during postharvest storage. The GA-ANN and ANFIS were fed with two inputs of methyl jasmonate and storage time. The results showed that GA-ANN predictions agreed with experimental data and the GA-ANN with 14 neurons in one hidden layer can predict physiological changes and quality parameters of pomegranate (weight loss, pH, chilling injury index, ion leakage, ethylene, respiration, polyphenols, anthocyanins, and total antioxidant activity) with correlation coefficients equal to 0.87. The ANFIS model was trained by a hybrid method and agreement between experimental data and ANFIS predictions was significant (r = 0.90).     

基于改进萤火虫算法的投影寻踪太湖水质综合评价

针对水质评价中指标的多样性及不相容性,提出了基于改进的萤火虫算法的投影寻踪水质评价方法。该方法根据水质标准等级样本集所划分的标准,构建投影指标函数,并用改进的萤火虫算法进行优化,使搜索步长逐步减小,寻优后期也可具有良好的局部搜索能力,得到最佳投影方向,使综合评价结果能够客观准确地反映出水质状况。运用该方法对太湖的水质数据进行综合评价,并与传统方法综合评分法(F值法)得到的结果进行对比,评价结果更符合当地水质状况实际情况,且对水质等级划分更为清晰。     

分块子空间追踪算法

压缩传感理论是一种充分利用信号稀疏性或者可压缩性的全新信号采样理论。该理论表明，通过采集少量的信号测量值就能够实现可稀疏信号的精确重构。本文在研究现有经典重构算法的基础上，提出结合图像分块思想和回溯思想的分块子空间追踪算法（Block Subspace Pursuit, B_SP）用于压缩传感信号的重构。该算法以块结构获取图像，利用回溯过程实现支撑集的自适应筛选，最终实现图像信号的精确重构。实验结果表明，在相同测试条件下，该算法的重构效果无论从主观视觉上还是客观数据上都有不同程度的提高。     

改进的Dijkstra算法及其在网络中的应用

提高计算机网络网络传输速度的关键是找到最佳的路由实现算法,本文从节约存储空间和提高运算效率方面对传统Dijkstra算法进行了改进,通过分析与比较,这种改进算法的效率优于传统的Dijkstra算法,特别适用于大规模网络。     

Landscape Zonation, benefit functions and target-based planning: Unifying reserve selection strategies

The most widespread reserve selection strategy is target-based planning, as specified under the framework of systematic conservation planning. Targets are given for the representation levels of biodiversity features, and site selection algorithms are employed to either meet the targets with least cost (the minimum set formulation) or to maximize the number of targets met with a given resource (maximum coverage). Benefit functions are another recent approach to reserve selection. In the benefit function framework the objective is to maximize the value of the reserve network, however value is defined. In one benefit function formulation value is a sum over species-specific values, and species-specific value is an increasing function of representation. This benefit function approach is computationally convenient, but because it allows free tradeoffs between species, it essentially makes the assumption that species are acting as surrogates, or samples from a larger regional species pool. The Zonation algorithm is a recent computational method that produces a hierarchy of conservation priority through the landscape. This hierarchy is produced via iterative removal of selection units (cells) using the criterion of least marginal loss of conservation value to decide which cell to remove next. The first variant of Zonation, here called core-area Zonation, has a characteristic of emphasizing core-areas of all species. Here I separate the Zonation meta-algorithm from the cell removal rule, the definition of marginal loss of conservation value utilized inside the algorithm. I show how additive benefit functions and target-based planning can be implemented into the Zonation framework via the use of particular kinds of cell removal rules. The core-area, additive benefit function and targeting benefit function variants of Zonation have interesting conceptual differences in how they treat and trade off between species in the planning process.     

遗传算法优化超声辅助低共熔溶剂提取红枣总黄酮工艺研究

本文采用一种新型绿色溶剂提取红枣总黄酮,并对其提取工艺进行优化。通过单因素试验探究含水量、超声功率、提取时间、提取温度和料液比对红枣总黄酮得率的影响。在此基础上,采用遗传算法优化超声辅助低共熔溶剂提取红枣总黄酮工艺。结果表明：超声辅助低共熔溶剂提取红枣总黄酮最优的工艺参数为：含水量37%、超声功率167 W、提取时间30 min、提取温度54 ℃和料液比1∶26（g/mL）。在此条件下,所得红枣总黄酮得率为（29.33±0.37）mg/g。试验值和理论值的相对误差为1.24%。表明遗传算法可较好地模拟和预测不同提取条件下红枣总黄酮得率,且优化工艺参数是可行的。研究发现低共熔溶剂可作为一种新型、绿色溶剂用于高效提取红枣总黄酮。[关键词] 遗传算法|红枣|总黄酮|低共熔溶剂|工艺     

基于随机森林的高寒草地地上生物量高光谱估算

草地地上生物量（Aboveground biomass,AGB）是衡量草地生产力的关键因素,准确测定草地AGB具有重要意义。高光谱因具有时效性强、不破坏草地等特点被广泛用于草地生理生态指标的测定。本研究提取和计算了海北试验站高寒草地冠层的原始光谱（Original spectrum,OR）反射率、一阶微分光谱（First derivative spectrum,FD）反射率、光谱位置面积参数（Spectral parameters of spectral position and area,PA）和植被指数（Vegetation indices,VI）4种不同类型的特征变量,使用连续投影算法（Successive projections algorithm,SPA）和递归特征消除算法（Recursive feature elimination,RFE）进行特征选择,采用随机森林算法（Random forest,RF）构建草地AGB估测模型。结果表明：在由4种特征变量分别构建的草地AGB估测模型中,基于VI的RF模型精度最高（测试集R²=0.70,RMSE=557.87 kg·ha^-1）,实测AGB与估测AGB的线性R²达到0.72;不同类型特征变量组合构建的草地AGB估测模型中,PA+VI组合的RF模型精度最高（R²=0.71,RMSE=548.97 kg·ha^-1）,实测AGB和估测AGB的线性R²达到0.73。     

基于模拟退火算法优化给水管线造价公式研究

给水管线造价公式的精确性对工程经济分析与给水管网优化的科学性和经济性有重大影响。通过五个地区的铸铁管综合单价数据,采用最小二乘法对各组数据进行多项式一至十次的拟合,确定了给水管线造价公式的最佳拟合次数为三次;进一步采用模拟退火算法对拟合的多项式参数进行优化计算,求解给水管线造价公式。结果表明:凭借模拟退火算法随机全局搜索模式以及不受函数性质影响的优势,能够克服传统算法难以求解多阶导数的困难,提高公式拟合精度。