SERVICE VALUE NETWORKS： INTO PRACTICE

Modeling a business to match its customer perceived （and customer desired） outcomes, remains an on-going task. This paper considers shows how a service value networks （SVNs） approach may be engaged to model, and deliver, understanding of the front-end business and its direct engagement with its immediate （or front-end） customers. These front-end customers may be either： （1） its off-line, in-store customers - engaging directly with the business sales staff （or representative）, or-（2） its on-line, virtual customers - engaging with the business via internet or remote access. In this situation, both the business, and the customer draw upon their respective viewpoints, and both sides influence the interaction. External to these business and customer influences are additional factors that capture the immediate and broader global effects - termed environmental effects. These three business-customer engagement areas of influence are captured by a SVN SEM approach.
Without a business-customer encounter of some kind the likelihood of a services business, and a prospecting customer, successfully engaging in an exchange process is reduced. This exchange may be a physical, and/or services exchange, and/or an information and/or ideas exchange. To the business, this encounter is, in effect, a trade, and as a result, the business targets acquiring an economic exchange that will ultimately deliver a net positive economic outcome. To the customer, external and internal information feeds, servicing, perceived value, and satisfaction, are key encounter drivers. SVNs offers a new way to understand the business-customer encounter, and to then utilize this acquired knowledge to either： （1） build a strategic management model, or （2） reengineer its business networks, and to then create a re-positioned, more customer-aligned business operation - one that is capable of delivering win - win, competitive business-customer solutions.     

神经网络在企业信用信息数据分析中的应用

以企业信用评级知识为基础，运用数据挖掘中的神经网络技术，研究企业信用评级中的神经网络结构，进行企业信用评级模型——神经网络感知器的新探索．以此制定相应的信用政策，控制企业的信用风险．     

人工神经网络的互连权空间的演化方程学习算法

本文把人工神经网络的互连权视为广义的自旋变量，网络的学习问题看作互连权空间的优化问题，进而将通常在人工神经网络组态空间的连续时间动力学方程组推广到人工神经网络的互连权空间，并在方程组中引入类似Ｍｅｔｒｏｐｏｌｉｓ的ＭｏｎｔｅＣａｒｌｏ算法机制改进此方程组了以提高寻优能力，提出了一个人工神经网络的演化方程学习算法，该算法在很大程度上摆脱了局域极值的束缚，得到最优或接近最优的互连权，本文集中研究单层反     

能量色散X荧光光谱和人工神经网络在甘肃新石器时代陶片分类研究中的应用

EDXRF spectromerty was applied to provenance studies of Neolitic Age(from 6000 BCto 2000 BC)pottery sherds excavated from Gansu Province in north-west China。Major and trace components analysis of te pottery specimen was carried out,and the artificial neural networks has been used to classift the trace com-ponent of these pottery。The results shown that pottery exchange had been existed during 3000 BC in ancient Gansu Province,China。     

3型FIR滤波器优化设计研究

详细研究了3型FIR线性相位滤波器的幅频响应与正弦基函数神经网络算法之间的关系,提出并证明了该模型算法的收敛性定理,给出了3型FIR带通滤波器、海尔伯特变换器和微分器的优化设计实例。研究结果表明提出的算法避免了求高价逆矩阵的困难,有效解决了3型FIR高价数字滤波器优化设计的瓶颈问题。计算机仿真结果表明了正弦基函数神经网络算法在3型FIR高价数字滤波器优化设计领域中的有效性。     

基于RBF神经网络的电力系统自适应励磁控制器的设计

采用后推设计算法设计了SISO严格反馈系统的RBF神经网络自适应控制器。权值的调整算法基于所选择的积分型的Lyapunov函数 ,能保证整个闭环系统是最终一致有界的。把所设计的控制方案用于电力系统的励磁控制中。仿真结果表明 ,所设计的控制器具有良好的跟踪性能和鲁棒性     

基于模糊神经网络的商业银行信用风险评估模型研究

在确立了商业银行信用风险评价指标体系的基础上,建立了基于模糊神经网络的商业银行信用风险评估模型.该网络具有四个因子输入,一个衡量商业银行信用风险的输出,总共六层的结构,且模糊规则层具有根据具体问题情况进行调节的能力,优于神经网络完全黑箱操作的特点.利用Matlab6.1对167组样本数据进行实证分析,训练结果表明网络预测误差小.     

神经网络反向传播模型用于CmI的能级分类

将神经网络反向传播算法应用于Ｃｕｒｉｕｍ的关于组态的分类计算。用４个特征变量（能级值、郎德ｇ日子、Ｊ量子数和同位素位移）表征每一个能好。为检验神经网络预报能力，采用ｌｅａｖｅ－ｎ－ｏｕｔ方法（奇宇称ｎ＝１，偶宇称ｎ＝５）对所有已知实验样本进行了预报。其中奇、偶宇称能级预报正确率分别为９２．７％和９７．４％。利用相同的神经网络结构，得到了１２个奇宇称和４２个偶宇称的未知能级的预报结果。     

露天矿边坡角的优化设计

提出了基于离散元法和神经网络的集成优化设计方法，离散元能充分考虑边坡岩体结构的不连续性，处理边坡的大变形情况，神经网络是从中获取经验知识，并用之解决问题的智能系统，该方法从两条途径，不同的角度，不同的侧面来设计边坡角，并进行优化，增强了设计的可行性。     

Color Perception of the Textile and Clothing

IntroductionColorisoneoftheimportantelementsinthecharacterizationoftextileandclothingproducts.Fashiondesignersandconsumersshowtheirtastesandstylesbycolorsandconcernaboutthesensationscreatedbycolors.Thebasisforthecolordifferentiation,inthiscase,cannotbeexplainedandsupportedbythetheoryofcolorimetricsystems.Colorperceptionisaveryspecialphenomenonandtheresultsofthepropertiesofthelightorsurface,theactivitiesofvisionsystemandcentralnervoussystem[1].Thereisagapbetweencolorandcolorperceptioninpractic…