中央空调系统节能的蚁群优化控制方法

针对中央空调系统运行时变性、非线性、多变量、大滞后等特性,提出了采用启发式全局寻优的蚁群优化算法对整个系统进行能耗寻优.通过建立空调系统能耗数学模型,采取蚁群算法对模型能耗最小化寻优,分析了信息素变挥发系数算法的收敛性,指出了算法以概率1收敛到最优解.同时对系统进行了控制结构改造及运行测试,结果表明,新的控制方法使空调系统能根据负荷变化进行参数动态调整,实现各设备运行匹配良好及工况较佳的目的,平均节能率达22.3%,获得较好的节能效果,对现有中央空调控制系统的节能改造具有借鉴作用.     

扩充编码符号的EZW改进算法

为了提高EZW算法的性能,分析发现EZW算法主扫描编码对最高频子带中不重要系数的编码存在冗余,且当重要系数的所有子孙系数都是不重要系数时的编码存在冗余.据此提出了扩充编码符号的EZW改进算法,用最高频子带的坐标作为限制条件来减少不重要系数的编码冗余;通过扩充编码符号和赋予原始EZW算法的4个编码符号新的适用范围的方案来减小第二种编码冗余.实验结果表明,本文算法使峰值信噪比提高0.9～2.3dB,从而证实了本文改进算法的有效性.     

A recognition method of vibration parameter image based on improved immune negative selection algorithm for rotating machinery

To overcome the limitations of traditional monitoring methods, based on vibration parameter image of rotating machinery, this paper presents an abnormality online monitoring method suitable for rotating machinery using the negative selection mechanism of biology immune system. This method uses techniques of biology clone and learning mechanism to improve the negative selection algorithm to generate detectors possessing different monitoring radius, covers the abnormality space effectively, and avoids such problems as the low efficiency of generating detectors, etc. The result of an example applying the presented monitoring method shows that this method can solve the difficulty of obtaining fault samples preferably and extract the turbine state character effectively, it also can detect abnormality by causing various fault of the turbine and obtain the degree of abnormality accurately. The exact monitoring precision of abnormality indicates that this method is feasible and has better on-line quality, accuracy and robustness.     

Application of ACO algorithm in protein structure prediction

The hydrophobic-polar (HP) lattice model is an important simplified model for studying protein folding. In this paper, we present an improved ACO algorithm for the protein structure prediction. In the algorithm, the "lone"ethod is applied to deal with the infeasible structures, and the "oint mutation and reconstruction"ethod is applied in local search phase. The empirical results show that the presented method is feasible and effective to solve the problem of protein structure prediction, and notable improvements in CPU time are obtained.     

Effective prediction of DEA model by neural network

In this paper,a fast neural network model for the forecasting of effective points by DEA model is proposed,which is based on the SPDS training algorithm.The SPDS training algorithm overcomes the drawbacks of slow convergent speed and partially minimum result for BP algorithm.Its training speed is much faster and its forecasting precision is much better than those of BP algorithm.By numeric examples,it is showed that adopting the neural network model in the forecasting of effective points by DEA model is valid.     

一类广义集值强非线性混合拟变分不等式(英文)

引进一类广义集值强非线性混合拟变分不等式,并且用投影技巧证明了它解的存在性.推广了广义集值强非线性混合变分不等式的许多结论.     

商务智能系统中两种改进的关联规则挖掘算法

根据日常商务数据的特点,提出了商业应用系统中进行数据挖掘的改进的Apriori算法和对点击流数据进行挖掘的改进的多支持度关联规则算法,并结合具体实例进行了分析.分析结果表明,2种改进的算法可以有效地提高系统数据挖掘的效率.     

基于关联分析的数据挖掘在体检CRM中的应用

运用关联分析方法对体检中心数据进行挖掘。首先分析了关联规则的原理和方法,然后对待挖掘数据进行集成、变换等预处理,得到能进行挖掘的原始数据.采用挖掘工具WEKA,选用关联规则中的Apriori算法对处理后数据进行分析,得到了与某一病症相关的各个症状之间的关联关系,以及各个症状与健康之间的关联关系。     

图像阈值自动选取算法的C＋＋实现

图像分割是运动目标检测与跟踪技术必须要解决的问题,在很多时候它涉及到图像阈值的选取。文章采用的算法首先计算图像中不同区域的灰度均值,结合最小差值条件,反复修订由计算所得的阈值,从而达到自动选优的目的。在此基础上,文章进一步给出了用C＋＋语言实现算法的关键代码。测试表明此方法选择阈值简便实用,图像的二值化效果良好。     

改进粒子群算法的动态空间调度方法

针对船体分段生产调度的多目标性和动态性,提出了一种改进粒子群算法的动态空间调度方法,确定船体分段在工作平台上的加工顺序和空间布局位置.算法以加工完成时间最短和空间利用率最高为目标,采用自适应惯性权重策略保证算法的收敛性,并引入遗传算法中的选择算子和变异算子增强算法的收敛速度和多样性,利用启发式定位策略确定分段的位置.最后,以船厂实际生产数据进行仿真验证.仿真结果表明,所提方法可以大大降低以手工方式制定调度计划的复杂度,并能有效地提高空间利用率达到70%,说明该方法是解决动态空间调度问题的一种有效方案.