Forward Generating Neural network (FGNN) is a special network for solving mapping problems.
前向生成神经网络是一种解决映射问题的神经网络。
This should take some of the "magic" out of Application Developer, and help you solve your own mapping problems when using the tools.
这样可以获得来自Application Developer的“魔力”，并且帮助您解决使用这些工具时的映射问题。
Mapping can solve all of these problems.
映射可以解决所有这些问题。
This tip introduces the notion of roundtripping and begins to hint at some problems with the existing JAX-RPC mapping rules.
本技巧介绍了往返的概念，并首先提示了一些与现有的JAX - RPC映射规则有关的问题。
Other authors 20 have defined a method for mapping and communicating stakeholder problems to business needs to system features (requirements) used for project scoping.
另外一些作者20已定义了一种方法来映射和交流涉众问题到业务需要再到项目的系统特性(需求)。
Instead, the focus should be on identifying commonalities between problems and then mapping them to technical solutions.
相反，应该将重点放在确定问题间的共性，然后将这些共性反映到技术解决方案中。
But there are a number of problems that cannot be solved by the mapping meta-data file.
但是有许多问题无法通过映射元数据文件解决。
then why can't we use XML as our first class data and programming model, given all the problems we learned the hard way with object-relational or OX mapping?
那么，考虑到我们在使用对象关系或OX映射的过程中发现的种种问题，为什么我们不能使用XML作为首选数据与编程模型呢？
they may cause problems when a mapping must name anonymous types;
当映射必须命名匿名类型时，它们可能会引起问题；
If your application is able to complete configuring NHibernate, you will know it had no problems with your mapping documents.
如果你的程序能够完成配置NHibernate，你就应该知道你的映射文件是没有问题的。
To avoid problems for non-U.S. countries, remove the mapping from BILLINGSTATE to BILLING_STATE.
为了避免来自非美国国家的问题，删除从BILLINGSTATE 到 BILLING_STATE的映射。
The method of mapping inversion model is a classical method of analyzing complicate problems.
映射反演模型方法是分析复杂问题的经典方法。
We always meet problems that how to construct the isomorphic mapping of two finite dimension linear Spaces in teaching.
在教学中，经常遇到如何构造两个有限维线性空间的同构映射的问题。
Network neural is fit for dealing with nonlinear problems especially for its good ability for nonlinear mapping.
神经网络具有良好的非线性映射能力，特别适合于处理各种非线性问题。
This paper probes into how to use mapping conception to solve concrete problems in teaching.
探讨了在教学过程中如何运用映射概念解决具体问题。
There are two problems when using mapping method to generate all quadrilateral meshes: one is difficulty of mesh transition; the other is low degree of automation.
利用映射法生成全四边形网格时存在两个问题：一是网格疏密过渡难，二是自动化程度不高。
Using the homotopy mapping theory, a class of nonlinear problems were studied.
利用同伦映射理论，本文研究了一类非线性问题。
Using homotopy methods, this paper gives an analysis on the existence of zeros of the decomposable mapping, and finds its application to a class of nonlinear eigenvalue problems.
用同伦方法分析了可分解映射的零点存在性，并获得它对一类非线性特征值问题的应用。
Comparing with the usual forward mapping methods, this algorithm generates derived images with less errors and tackles the problems associated with multiple reference images.
与通常的正向映射算法相比，该算法克服了多幅参考图像所带来的计算量成倍增长等问题，而且误差较小。
This thesis focuses on the development of an indoor autonomous mobile robot, obstacle recognition and SLAM (Simultaneous Localization and Mapping) problems for indoor mobile robot.
本文主要针对室内自主式移动机器人开发及其室内环境障碍物识别和同时定位与地图创建问题开展研究工作。
EPSW Electric mapping System is applied widely, the platform impetuses and solves the unresolved problems in surveying and mapping domain and GIS front profession.
电子平板测绘系统已得到了较广泛的应用，该平台的推出推动并解决了测绘行业及GIS前端行业多年来悬而未决的问题。
Through analyzing the main contents and features of field operation and indoor operation in digital mapping, this paper advances some problems needing attention in digital mapping work.
通过对数字测图外业、内业工作的主要内容与特点的分析，提出了数字测图工作中需注意的几个问题。
To the problems that influence the precision in digital house property surveying and mapping data collection and area measurement, the paper offers corresponding methods to resolve them.
房产测量外业数据采集及面积计算是影响房产测量精度的重要因素，就实际工作中在这两个方面容易出现的问题提出了相应的解决方法。
The problems crossed among neuronal modeling, functional brain and brain mapping are also described in this paper.
本文还讨论了神经元建模、脑功能和功能成像三者之间的交叉问题。
Kernel Methods are concerned with mapping input data into a higher dimensional vector space where some classification or regression problems are easier to model.
核函数方法关心的是如何把输入数据映射到一个高维度的矢量空间，在这个空间中，某些分类或者回归问题可以较容易地解决。
Kernel Methods are concerned with mapping input data into a higher dimensional vector space where some classification or regression problems are easier to model.
核函数方法关心的是怎样把输入数据映射到一个高维度的矢量空间，在这个空间中，某些分类或者回归问题可以较容易地解决。
Kernel Methods are concerned with mapping input data into a higher dimensional vector space where some classification or regression problems are easier to model.
核函数方法关心的是怎样把输入数据映射到一个高维度的矢量空间，在这个空间中，某些分类或者回归问题可以较容易地解决。
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