基于人工神经网络的客户价值评价体系

企业对客户价值的评价是进行客户分类及实施客户策略的依据,也是实现客户关系管理的核心所在.客户为企业带来的价值本身是具有层次性和动态性,然基于客户全生命周期的客户终生价值计算方法显然无法将其动态发展过程准确的考虑在内,也就无法规避其计算结果所带来的误差风险.因此,在现有客户价值评价理论的基础上,结合客户生命周期理论,以企业与客户的双赢为最终目标,提出分阶段细化的评价思想,构建完善的评价体系,并根据该指标体系的指标特性选择人工神经网络作为评价方法,给出模型求解,最后依据此评价结果对客户分类及营销策略进行相关的分析.     

截击机机载武器装备最优规划的遗传算法研究

机载武器的合理配置是飞机作战效能得以充分发挥的前提条件.本文给出了截击机拦截多目标时的效能评估指标及其与机载武器系统之间的关系模型,并以遗传算法确定了截击机作战效能与武器装备及相关飞行参数的量化关系.经过仿真计算成功地解决了截击机在一定作战条件下,效能指标达到最大时武器装备的最优规划问题.     

低碳经济范式下能源结构优化程度评价研究

我国的能源瓶颈突出表现为结构性矛盾。本文以能源经济学为理论支撑,从低碳经济发展对能源结构要求的角度,提出了社会经济效益、能源规划效益、环境效益等三个维度,并分析了各个维度的构成要素;综合运用粗糙集理论和AHP法进行指标约简和权值计算,构建了低碳经济范式下能源结构优化程度评价指标体系。在此基础上,对各评价指标设置分级标准,通过未确知测度评价模型对2010年我国10个省份的能源结构优化程度进行多指标综合测度与评价,阐明了该评价方法的科学有效性及评价结果的合理性。     

基于BP神经网络误差函数优化算法的武器装备承制商信用评价模型研究

信用评价是选择武器装备承制商的重要手段.以国标为基础,结合承制商具体情况确定了信用评价指标体系.分析了传统信用评价方法的不足,对经典BP神经网络的误差函数进行优化,优化后的网络模型收敛速度更快,预测精度更高.构建BP神经网络武器装备承制商信用评价模型,仿真实验表明武器装备承制商信用评价可以选用BP神经网络模型.     

改进的公共权重的区间DEA效率评价

提出了一种改进的公共权重的考虑非期望产出的区间数据包络模型(DEA)评价模型.通过Pearson相关性检验,VIF检验法和容忍度检验法对投入,期望产出和非期望产出指标进行约简,并计算出指标的客观权重,引入调和参数使客观权重满足DEA模型限制条件且存在最优解.该模型有效解决了传统DEA模型中可能发生的公共权重分配为零的情况发生,同时考虑了指标负面评价的情况,增加评价结果的区分度.对中国北方十个省份的矿业环境治理效率进行了实证分析,构建了矿山生态环境治理效率评价体系,与相关文献进行比较,表明该模型的有效性且可以有效降低计算复杂度,提高计算效率.     

基于T-FAHP的线上购物顾客感知价值评价研究——以A购物平台为例

针对线上购物环境,明确基于平台-产品-顾客3类要素的顾客感知价值评价指标体系.使用三角模糊层次分析法(T-FAHP)构建评价模型,通过计算确定各指标权重值,对指标体系中的指标分五级变量赋予效用值,得到顾客感知价值评价分数.以A购物平台为例,验证评价模型的可行性.发现A平台主要优势在于产品价格以及降低了顾客对购物风险的感知.通过对指标元素分析解读,帮助线上购物企业制定竞争策略,提升顾客感知价值.     

基于模糊评价法的企业经理人的绩效评估

为了建立一套对企业经理人的绩效进行科学、合理检测的评价体系,首先建立了一套反映企业经理人绩效的二级指标体系,然后构建了一个评价企业经理人绩效的模糊综合评价模型,并将该模糊综合评价模型应用到一个具体的企业,得到了该企业经理人各绩效指标的权重,再通过对该企业具体经理人对应的各二级绩效指标进行评价,然后采用模糊算法计算得到了该经理人的综合绩效分.该评价体系简单方便,有助于企事业单位对自己的高级员工进行有效的人事管理.     

基于云模型的三维土工网防护边坡侵蚀稳定性综合评价

在分析各项影响因素分级标准的基础上,建立了三维土工网护坡侵蚀稳定性的综合评价体系.针对三维土工网边坡侵蚀稳定性分析中不确定性问题,将云模型理论引入到综合评判中,实现了定性与定量的不确定性转换.通过构建前件云发生器和后件云发生器,利用不确定推理,确定各项评价指标的具体分值.借助层次分析法与敏感度分析确定各项评价指标的权重,进而得出综合评价体系的整体得分.工程实例计算结果证实了模型的实用性和有效性,为三维土工网防护边坡侵蚀稳定性的评价提供了一条新的途径.     

神经网络在大型复杂武器装备质量综合评价排序中的应用

从大型复杂武器装备系统的具体特点出发,运用基于神经网络的评价方法,构建了大型复杂武器装备质量综合评价模型。实证分析的结果表明该方法得出的结果与实际情况相符。     

基于灵敏度分析的职业环境健康综合评价方法

职业环境综合评价是一种动态的评估模型,而传统的评价方法只能得到静态的固定评级或结果值,无法反映出系统的稳定性和评价的真实性.而灵敏度分析法可以反映出评价体系内各个指标的变化对最终评价结果的影响程度,即关联性,并且其适用与实际中的动态评价体系.针对传统方法中存在的缺点,提出基于灵敏度分析的职业健康综合评价方法:首先,利用熵权系数法计算出评价体系内各个指标的权重,其次,根据指标权重灵敏度分析,分别计算出各个指标的权重阈值变化范围.结果表明,该评价方法能够准确实际地反映出真实职业环境的评价结果,并且可以明显地观察到各个指标的变化阈值大小,为后续的政策制定和环境管理办法的提出,提供了有力的支持和帮助,适合在公共卫生领域实施和推广.     

Simple,efficient heuristics for multi-item multi-location spare parts systems with lateral transshipments and waiting time constraints

This paper deals with the analysis of a multi-item, continuous review model of a multi-location inventory system of repairable spare parts, in which lateral and emergency shipments occur in response of stock-outs. The objective is to determine close-to-optimal stocking policies minimizing the total cost for inventory holding, lateral transshipments, and emergency shipments subject to a target level for the average waiting times at all locations. We structure the optimization problem as a combinatorial problem and four different heuristics are developed and evaluated in terms of their total costs and computation times. It is shown that the greedy-type heuristic has the best performance. A numerical study is carried out to look at the relative cost savings obtained from the use of multi-item approach and lateral transshipments.     

A hybrid deep computation model for feature learning on aero-engine data: applications to fault detection

Recently, the safety of aircraft has attracted much attention with some crashes occurring. Gas-path faults, as the most common faults of aircraft, pose a vast challenge for the safety of aircraft because of the complexity of the aero-engine structure. In this article, a hybrid deep computation model is proposed to effectively detect gas-path faults on the basis of the performance data. In detail, to capture the local spatial features of the gas-path performance data, an unfully connected convolutional neural network of one-dimensional kernels is used. Furthermore, to model the temporal patterns hidden in the gas-path faults, a recurrent computation architecture is introduced. Finally, extensive experiments are conducted on real aero-engine data. The results show that the proposed model can outperform the models with which it is compared.     

跳-扩散过程下具有目标杠杆比率的违约风险模型

本文在研究公司债务违约风险时,假设公司价值的动态变化服从跳-扩散过程;假设公司可以根据公司价值的变化调整其债务水平,因而存在公司的目标杠杆比率,违约边界定义为公司历史价值的对数加权平均;当公司价值下降到违约边界时发生债务违约.数值模拟表明公司债务的信用利差对公司的目标杠杆比率和跳过程的强度具有高度的敏感性.本文的模型解决了在长期和短期信用利差预测时结构化模型和约化模型存在的缺陷.     

ACUTA: A novel method for eliciting additive value functions on the basis of holistic preference statements

In multiple criteria decision aiding, it is common to use methods that are capable of automatically extracting a decision or evaluation model from partial information provided by the decision maker about a preference structure. In general, there is more than one possible model, leading to an indetermination which is dealt with sometimes arbitrarily in existing methods. This paper aims at filling this theoretical gap: we present a novel method, based on the computation of the analytic center of a polyhedron, for the selection of additive value functions that are compatible with holistic assessments of preferences. We demonstrate the most important characteristics of this technique with an experimental and comparative study of several existing methods belonging to the UTA family.     

Asymptotic behavior of Morris–Lecar system

In this paper, the asymptotic behavior of the Morris–Lecar model is studied. Firstly, the phase plane characteristic of the model is investigated, secondly, in terms of the concepts of slow manifold and fast foliation, the asymptotic structure of the Morris–Lecar model is discussed based on Tikhonov's theorem. Finally, computation and analysis results show that the spiking of action potential of Morris–Lecar neuron actually has the jump onset–jump return structure.     

A New Scheme for the Tensor Representation

The paper presents a new scheme for the representation of tensors which is well-suited for high-order tensors. The construction is based on a hierarchy of tensor product subspaces spanned by orthonormal bases. The underlying binary tree structure makes it possible to apply standard Linear Algebra tools for performing arithmetical operations and for the computation of data-sparse approximations. In particular, a truncation algorithm can be implemented which is based on the standard matrix singular value decomposition (SVD) method.     

Simulation of Drug Uptake in a Two Compartmental Fractional Model for a Biological System

This paper presents a very effective numerical method for the solution of the two-compartmental pharmacokinetic model for oral drug administration. This model consists of a set of two fractional order differential equations which connect the two compartments. The first compartment represents the gut while the second compartment corresponds to the drug concentration in the target tissue. For ease of computation, the numerical solution is also created as a Matlab function.     

Rotorcraft parameter estimation using radial basis function neural network

Increased emphasis on rotorcraft performance and operational capabilities has resulted in accurate computation of aerodynamic stability and control parameters. System identification is one such tool in which the model structure and parameters such as aerodynamic stability and control derivatives are derived. In the present work, the rotorcraft aerodynamic parameters are computed using radial basis function neural networks (RBFN) in the presence of both state and measurement noise. The effect of presence of outliers in the data is also considered. RBFN is found to give superior results compared to finite difference derivatives for noisy data.     

Spatial Regression Models for Extremes

Meteorological data are often recorded at a number of spatial locations. This gives rise to the possibility of pooling data through a spatial model to overcome some of the limitations imposed on an extreme value analysis by a lack of information. In this paper we develop a spatial model for extremes based on a standard representation for site-wise extremal behavior, combined with a spatial latent process for parameter variation over the region. A smooth, but possibly non-linear, spatial structure is an intrinsic feature of the model, and difficulties in computation are solved using Markov chain Monte Carlo inference. A simulation study is carried out to illustrate the potential gain in efficiency achieved by the spatial model. Finally, the model is applied to data generated from a climatological model in order to characterize the hurricane climate of the Gulf and Atlantic coasts of the United States.