上市公司财务比率指标的非线性主成分分析

本文选取上海证券市场和深圳证券市场共400家上市公司,利用非线性主成分分析方法对这些上市公司的财务比率指标进行综合评价,以考察上市公司的财务状况。     

黎曼流形中一类半线性方程的群分析

本文对n维平坦流形中的一类线性和半线性方程进行群分析,给出了这类方程为共形不变的充要条件。     

扬州市肺癌发病风险因素的Logistic回归分析

取扬州市中医院2010年1月-2013年12月住院治疗的肺癌患者,共425例,对照组为同期来院体检或探视病人的家属,共425例,采用自行设计的问卷进行调查,并使用Logistic回归分析来进行统计,分析扬州市肺癌病人发病的风险因素。结果表明,肺部疾病对肺癌发病率的影响最大,然后依次是内向忧郁的不良情绪,吸烟因素,患有高血压疾病;经常食用新鲜蔬菜瓜果则是肺癌的保护性因素.给出建议:应培养乐观积极的心态,大力提倡健康的生活方式,多吃新鲜蔬果,适当进行一些体育锻炼,加强控烟力度,防治慢性病.     

基于计量学共词视角的突发事件应对研究知识图谱与热点主题分析

以2006-2015年CNKI知识库CSSCI期刊的1875篇文献作为样本来源,利用可视化软件CiteSpaceⅢ对国内突发事件应对方面的研究成果进行知识图谱呈现和文献计量分析,直观呈现作者共现图谱、关键词共现图谱和Time Zone图谱,并就突现词等研究热点及演化路径进行分析,说明“突发事件应对”近十年的研究发展情况.指出研究力量分散,研究主体以社会事件为风向,提出了“突发事件常态化”与“突发事件应对合理化”之间的耦合度量的问题.     

基于DEA的基金绩效评价研究

用数据包络分析(DEA)方法对天天基金网中最新银河评级一年期表现优秀的三大类共39只基金绩效进行了进一步的评价研究,选取能充分体现基金风险与收益的统计指标,均值与方差,运用DEA有效性原理比较并分析了39只基金的技术有效性与规模有效性,给出了无效基金仿效的标杆.     

职业认同、团队认同对员工建言行为影响的实证研究

基于社会认同理论,试图考察职业认同和团队认同对员工建言行为的影响及其交互作用.研究共收集347份有效问卷,数据处理主要运用了相关性分析、内部一致性信度分析、验证性因子分析以及层次回归分析.结果表明,在控制人口统计学变量和持续承诺后,职业认同和团队认同的提高均能有效促进员工做出建言行为;同时职业认同和团队认同对建言行为存在交互作用:在低职业认同水平下,团队认同与建言行为的正相关关系比高职业认同水平下更强.     

层次分析法在步枪作战效能评估中的应用

应用层次分析法对两代(共10种)突击步枪和自动步枪作战效能的评估和排序进行了讨论,并对其作战效能和技术水平进行了判断.所得结论对了解我军装备的先进程度,适应新时期军事科学和战备具有特别重要的意义.     

我国人口时间序列拟合模型的比较与选择

通过对我国1990年至2007年共18年的人口数据进行实证分析,运用时间序列的三个不同模型,对我国人口的变化规律进行了拟合研究;并给出了反映各个模型拟合精度的AIC值和SBC值;最后,通过对所建模型的比较分析,对拟合模型的选择提出了相关建议.     

广西GDP的统计预测模型及其应用

本文利用SPSS统计软件及非参数统计方法(卡方检验和K-S检验法)对广西1950年至2006年共57年的GDP数据进行实证分析.在最佳准则(即AIC准则)下建立了ARIMA(1,2,1)时间序列模型,并利用非参数统计方法对此模型进行了适应性检验,然后利用2001年至2006年的实际值与该模型的预测值进行了比较.最后,本文利用该模型对广西未来五年的GDP进行了预测.     

扬州地区食道癌发病风险因素分析

选取2012-2015年间在扬州地区某医院住院治疗的食道癌患者,共500例,对照组为同期来院体检或探视病人的家属,共500例,自行设计问卷进行调查,并使用Logistic回归分析来进行统计,分析扬州地区食道癌病人发病的风险因素.结果表明,食管类其他疾病对食道癌发病率的影响最大,然后依次是内向忧郁的情绪因素,吸烟,霉变腌制品,进食快、喜热、辛辣食品等因素,而经常食用新鲜水果蔬菜则是食道癌的保护性因素.给出建议:应养成良好的饮食习惯,培养乐观积极的心态,注重营养平衡,多吃新鲜水果蔬菜,加强控烟力度,防治慢性病.     

LAHS: A novel harmony search algorithm based on learning automata

This study presents a learning automata-based harmony search (LAHS) for unconstrained optimization of continuous problems. The harmony search (HS) algorithm performance strongly depends on the fine tuning of its parameters, including the harmony consideration rate (HMCR), pitch adjustment rate (PAR) and bandwidth (bw). Inspired by the spur-in-time responses in the musical improvisation process, learning capabilities are employed in the HS to select these parameters based on spontaneous reactions. An extensive numerical investigation is conducted on several well-known test functions, and the results are compared with the HS algorithm and its prominent variants, including the improved harmony search (IHS), global-best harmony search (GHS) and self-adaptive global-best harmony search (SGHS). The numerical results indicate that the LAHS is more efficient in finding optimum solutions and outperforms the existing HS algorithm variants.     

城市生态化状况的协调性评价

协调性评价问题是系统评价中的一类重要研究课题,本文给出了城市生态化状况协调性评价的数理统计方法,并对江苏省十个城市的生态化状况协调性进行了评价。     

Decision-theoretic harmony: A first step

This paper presents a novel harmony generation method based on decision-theoretic planning. We are the first to model music generation using Markov decision processes (MDPs). We give a proof of concept for this approach by using MDP planning to generate four-part harmony, given the melody or soprano line. Our initial results show feasibility, and show the variance possible, depending on the choice of reward functions.     

Parameter adaptive harmony search algorithm for unimodal and multimodal optimization problems

This paper presents a parameter adaptive harmony search algorithm (PAHS) for solving optimization problems. The two important parameters of harmony search algorithm namely Harmony Memory Consideration Rate (HMCR) and Pitch Adjusting Rate (PAR), which were either kept constant or the PAR value was dynamically changed while still keeping HMCR fixed, as observed from literature, are both being allowed to change dynamically in this proposed PAHS. This change in the parameters has been done to get the global optimal solution. Four different cases of linear and exponential changes have been explored. The change has been allowed during the process of improvization. The proposed algorithm is evaluated on 15 standard benchmark functions of various characteristics. Its performance is investigated and compared with three existing harmony search algorithms. Experimental results reveal that proposed algorithm outperforms the existing approaches when applied to 15 benchmark functions. The effects of scalability, noise, and harmony memory size have also been investigated on four approaches of HS. The proposed algorithm is also employed for data clustering. Five real life datasets selected from UCI machine learning repository are used. The results show that, for data clustering, the proposed algorithm achieved results better than other algorithms.     

Two improved harmony search algorithms for solving engineering optimization problems

This paper describes two new harmony search (HS) meta-heuristic algorithms for engineering optimization problems with continuous design variables. The key difference between these algorithms and traditional (HS) method is in the way of adjusting bandwidth (bw). bw is very important factor for the high efficiency of the harmony search algorithms and can be potentially useful in adjusting convergence rate of algorithms to optimal solution. First algorithm, proposed harmony search (PHS), introduces a new definition of bandwidth (bw). Second algorithm, improving proposed harmony search (IPHS) employs to enhance accuracy and convergence rate of PHS algorithm. In IPHS, non-uniform mutation operation is introduced which is combination of Yang bandwidth and PHS bandwidth. Various engineering optimization problems, including mathematical function minimization problems and structural engineering optimization problems, are presented to demonstrate the effectiveness and robustness of these algorithms. In all cases, the solutions obtained using IPHS are in agreement or better than those obtained from other methods.     

群体决策的偏好协调性检验

对于一类群体决策问题，本文引进群体的偏好协调性指标，并且给出了偏好协调性指标的统计检验．在此基础上，还提出一个求该类群体决策问题的方法，以及讨论了群体的偏好快调性指标和群体决策结果间的关系．     

A Comparative Investigation on Model Selection in Independent Factor Analysis

With uncorrelated Gaussian factors extended to mutually independent factors beyond Gaussian, the conventional factor analysis is extended to what is recently called independent factor analysis. Typically, it is called binary factor analysis (BFA) when the factors are binary and called non-Gaussian factor analysis (NFA) when the factors are from real non-Gaussian distributions. A crucial issue in both BFA and NFA is the determination of the number of factors. In the literature of statistics, there are a number of model selection criteria that can be used for this purpose. Also, the Bayesian Ying-Yang (BYY) harmony learning provides a new principle for this purpose. This paper further investigates BYY harmony learning in comparison with existing typical criteria, including Akaik’s information criterion (AIC), the consistent Akaike’s information criterion (CAIC), the Bayesian inference criterion (BIC), and the cross-validation (CV) criterion on selection of the number of factors. This comparative study is made via experiments on the data sets with different sample sizes, data space dimensions, noise variances, and hidden factors numbers. Experiments have shown that for both BFA and NFA, in most cases BIC outperforms AIC, CAIC, and CV while the BYY criterion is either comparable with or better than BIC. In consideration of the fact that the selection by these criteria has to be implemented at the second stage based on a set of candidate models which have to be obtained at the first stage of parameter learning, while BYY harmony learning can provide not only a new class of criteria implemented in a similar way but also a new family of algorithms that perform parameter learning at the first stage with automated model selection, BYY harmony learning is more preferred since computing costs can be saved significantly.     

A harmony search algorithm for university course timetabling

One of the main challenges for university administration is building a timetable for course sessions. This is not just about building a timetable that works, but building one that is as good as possible. In general, course timetabling is the process of assigning given courses to given rooms and timeslots under specific constraints. Harmony search algorithm is a new metaheuristic population-based algorithm, mimicking the musical improvisation process where a group of musicians play the pitches of their musical instruments together seeking a pleasing harmony. The major thrust of this algorithm lies in its ability to integrate the key components of population-based methods and local search-based methods in a simple optimization model. In this paper, a harmony search and a modified harmony search algorithm are applied to university course timetabling against standard benchmarks. The results show that the proposed methods are capable of providing viable solutions in comparison to previous works.     

Navigation of underwater robot based on dynamically adaptive harmony search algorithm

The current research work has employed an evolutionary based novel navigational strategy to trace the collision free near optimal path for underwater robot in a three-dimensional scenario. The population based harmony search algorithm has been dynamically adapted and used to search next global best pose for underwater robot while obstacle is identified near about robot’s current pose. Each pose is evaluated based on their respective value for objective function which incorporates features of path length minimization as well as obstacle avoidance. Dynamic adaptation of control parameters and new perturbation schemes for solution vectors of harmony search has been proposed to strengthen both exploitation and randomization ability of present search process in a balanced manner. Such adaptive tuning process has found to be more effective for avoiding early convergence during underwater motion in comparison with performances of other popular variants of Harmony Search. The proposed path planning method has also shown better navigational performance in comparison with improved version of ant colony optimization and heuristic potential field method for avoiding static obstacles of different shape and sizes during underwater motion. Simulation studies and corresponding experimental verification for three-dimensional navigation are performed to check the accuracy, robustness and efficiency of proposed dynamically adaptive harmony search algorithm.     

Novel selection schemes for harmony search

Selection is a vital component used in Evolutionary Algorithms (EA) where the fitness value of the solution has influence on the evolution process. Normally, any efficient selection method makes use of the Darwinian principle of natural selection (i.e., survival of the fittest). Harmony search (HS) is a recent EA inspired by musical improvisation process to seek a pleasing harmony. Originally, two selection methods are used in HS: (i) memory consideration selection method where the values of the decision variables are randomly selected from the population (or solutions stored in harmony memory (HM)) to generate a new harmony, and (ii) selecting a new solution in HM whereby a greedy selection is used to update the HM. The memory consideration selection, the focal point of this paper, is not based on natural selection principle which draws heavily on random selection. In this paper, novel selection schemes which replace the random selection scheme in memory consideration are investigated, comprising global-best, fitness-proportional, tournament, linear rank and exponential rank. The proposed selection schemes are individually altered and incorporated in the process of memory consideration and each adoption is realized as a new HS variation. The performance of the proposed HS variations are evaluated and a comparative study is conducted. The experimental results using benchmark functions show that the selection schemes incorporated in memory consideration directly affect the performance of HS algorithm. Finally, a parameter sensitivity analysis of the proposed HS variations is analyzed.