基于算法的定量式图书流通统计分析方法研究

以广东石油化工学院图书馆各类图书的历史流通数据为依托,利用数据分析方法,统计读者借阅需求,依据读者需求明确图书采购种类,从源头满足图书流通量;引入公式和数据分析,实现合理设置查询系统以及书籍摆放策略,帮助读者准确定位图书位置,加快图书流通效率。文章尝试以算法探求图书流通的内部规律,加强图书现代化建设,促进图书馆流通管理。     

基于方格网法的树冠体积测算方法

提出了一种基于方格网法测算树冠体积的方法。研究利用免棱镜全站仪获取树冠点云数据,建立树冠表面特征点的坐标方格网,根据方格网法计算土方量的原理来求算树冠体积,通过传统经典模型人工方法、三维激光量测法与本文算法,分别计算实验区域研究对象的体积,将结果进行对比分析,本研究提出的算法平均相对误差为4.13%,平均准确度为95.97%,且在置信度0.05下树冠体积的计算值与实测值之间的差异性为不显著,结果完全满足计算精度,其计算耗时只有传统人工量测法的1/3,成本为三维激光扫描的2%,降低了成本且提高了效率。     

基于模拟退火和遗传算法的神经网络在精确施肥的研究

为了更加精确地描述农作物产量与土壤和施肥量中的N、P、K浓度之间的复杂的非线性关系,对原始的BP神经网络进行了改进。首先采用模拟退火算法对神经网络的初始权值和阈值进行优化,提高了网络的整体逼近性能,再用遗传算法对神经网络的权值和阈值进行改善,并对这两种方法的优化效果进行了比较,结果表明模拟退火和遗传算法的神经网络能产生很好的效果。     

机动车尾气排放测算行驶周期的自动建立方法

为了建立用于机动车尾气排放测算的行驶周期,利用便携车载尾气采集装置(PEMS)系统和GPS装置进行了大量的机动车尾气排放和驾驶活动数据采集,并对数据进行了预处理,提出了短行程的评价标准,给出了最优短行程自动搜索的2种启发式算法(遗传算法(GA)和离散同步扰动随机逼近(DSPSA)),对建立的行驶周期进行了预测尾气排放总量的有效性验证。结果表明：2种启发式算法均能有效地解决行驶周期自动建立问题,GA算法优化得到的行驶周期更有代表性,该行驶周期能以相对较小的误差预测尾气排放总量。建议将GA算法作为开发其他城市的行驶周期的算法工具。     

Design of Structural Optimization Based onAdaptive Genetic Algorithm

In this paper, a simple genetic algorithm (GA) is presented for optimizing the structural systems with discrete design variable. First, the origin, basic process of GA was discussed. In order to improve the convergence of GA, considering the difficulty in choosing the probability of crossover and mutation, an adaptive GA, in which the probability of crossover and mutation were varied depending on the fitness values of the solutions, was introduced. Finally, the availability of the adaptive GA was shown by an example.     

用模糊神经网络提高洪峰预报精度的研究

在大量研究的基础上,提出了基于模糊理论的神经网络改进算法,用来提高对洪峰的预报精度。该方法在网络训练时引入模糊理论来确定网络误差修改的程度。引入的算法增大了大值输出样本和期望输出的误差,使得网络向着提高洪峰拟合精度的方向修改权重。应用表明,改进的模糊BP神经网络能够较好的反映洪水演进机理,提高了神经网络洪水预报模型对洪峰的预报精度,保证了洪峰预报的可靠性。     

单位矩阵解耦控制法在纸机网前箱系统中的应用

运用单位矩阵解耦控制方法,解决造纸过程中纸机网前箱系统中纸浆流量和蒸汽压力的耦合问题;并在实验室的纸机网前箱的模型中予以实际应用,实验结果表明:该解耦控制算法是可行的,有一定实用价值。     

Probabilistic approaches to scheduling reserve selection

Most existing reserve selection algorithms are static in that they assume that a reserve network is designed and patches are selected by decision-makers at a single point in time. In reality, however, selection processes are often dynamic and patches are selected one by one or in several groups because for example there are insufficient funds at the beginning of the process to put all the patches under protection. Finding an optimal dynamic selection strategy is tricky since due to the complementarity principle the value of a particular patch depends on the presence of other patches in the network - including those that have not yet been selected. As unprotected patches may be lost, e.g., through development, the long-term value of selecting a particular patch is uncertain. Existing dynamic selection algorithms are either ‘myopic’ and consider only those patches that have already been protected, totally ignoring future uncertainty, or they are based on stochastic dynamic programming, which delivers the optimal strategy taking uncertainty into account but is numerically too complex to be employed in actual selection problems. In this paper, a ‘foresighted’ selection strategy as well as a number of variants are developed using probability theory. The different strategies are compared for a large number of selection problems. All variants outperform the myopic strategy and perform close to the optimal strategy. However, the performances of all strategies, including the optimal and the myopic one, are not dramatic.     

温室机器人驱动控制系统的算法研究

为了使机器人的行动能适应温室的复杂情况,提高机器人的运动控制精度,将模拟PID控制算法离散化,对编码反馈误差进行归一化处理,并将其输入3层的BP神经网络,研究了隐含层加权系数的计算方法,完成并验证了模糊BP神经网络PID控制系统的算法。结果证明,在同一指令时间内,模糊神经网络控制器能够很好地完成角度指令的零误差调节,与常规PID控制器相比较,模糊神经网络控制器的超调量显著减小。     

应用遗传算法优化设计汽车散热器

根据减少耗材的要求,本文将遗传算法应用于管带式汽车散热嚣的优化设计。提出了散热器芯体结构参数优化的方案,详细讨论了优化算法的步骤及流程,最后,通过实例表明该方法能得到较好的优化结果。