彩票走势图

NeuroSolutions的主要功能

• 输入投影
  通过自动将多条信息映射至单一输入，进一步减小了输入的规模

• 输入优化
  通过贪婪搜索回除法和其他方法自动决定最有用的输入

• CUDA GPU处理
  NeuroSolutions的用户可以通过使用NeuroSolutions CUDA插件管理NVIDIA显卡的强大处理能力

• 更快的处理速度
  软件改进了对多核处理器的使用，优化了可执行编码，这都使得训练时间极大的减少

• 支持向量机回归
  支持向量机回归（SVM-R）

• 增强了随机神经网络的支持

• 神经模糊
  其神经模糊系统(CANFIS)模型集成了神经网络的模糊输入，一边快速的解决模糊定义的问题

• 支持向量机
  其支持向量机（SVM）模型将输入映射入一个大尺寸的特征空间，然后通过对与数据边界叫相近的输入数据进行隔离，以最优化的将数据分离入其相应的类中。这在分离那些共享着复杂的边界的数据集尤其有效。

• Levenberg-Marquardt
  第二序列学习算法较原动力学习算法在速度上有了相当大的提高，并且往往出错率更低

• 导师强迫/迭代预测
  有一些时间序列问题能通过一种被称为“导师强迫”的方式进行最佳模式化处理。为提高多部预测的准确率，这种特殊的训练算法将预测的输出结果反馈入了输入中。

临时神经网络
NeuroSolutions是当前少数几种完全支持通过时间反向传播（BPTT）的神经网络开发工具之一。其与传统的将静态输入映射入一个静态输出不同，BPTT可以将一系列输入映射入一系列输出中，这使得其可以通过提取数据每次的变化来解决临时的问题。

用户自定义的神经拓补结构
NeuroSolutions是基于以下内容而应用的，即神经网络可以分解为一个神经组件的基础性集合。每一个单独的组件都是相对简单的，但是将多个组件连接起来以后，其即可组成网络以解决相当复杂的问题。网络组建向导可以根据用户指定的条件为之连接相应的组件。然而，一旦该网络创建好了，用户即可任意的改变其相互联系或者添加入新的组件，换而言之，即几乎可以创建无限的神经模型。

用户自定义的神经组件
每一个NeuroSolutions组件都应用了一个函数以遵循一个C编写的简单协议。如需添加一个新的组件，用户只需简单的修改基础组件的模板函数，然后将其代码编译为一个DLL文件---这一切都可以在NeuroSolutions中完成！

C++代码生成
通过使用NeuroSolutions开发者层级，应用程序开发员可通过使用自定义解决方案向导生成DLL或为网络生成C++源码的方式将NeuroSolutions神经网络集成入其应用程序中。该NeuroSolutions代码生成工具如同其面向对象的开发环境一样稳健。无论您在图形用户界面中创建的神经网络是多么的简单或者复杂，NeuroSolutions都能生成等价的ANSI C++源码的神经网络—即使这些神经网络中以DLL的方式含有您自己设计的算法。

大量的探索功能
神经网络因为其“黑箱子”技术经常被用户批评，但NeuroSolutions提供了大量通用的探索工具集，用户便再也无需担心这种情况的发生了。探索工具使得用户可以实时的访问内部网络参数，比如：

• 输入/输出

• 权重

• 错误

• 隐藏状态

• 渐变

• 敏感性

 探索在神经网络设计中是非常重要的一步，因此我们将之处理成为NeuroSolutions中集成的一部分。和神经组件一样，探索组件也是模块化的，用户浏览数据的方式与数据展现的形式无关。所有的神经网络数据都是通过一个通用协议进行报送的，且所有的 NeuroSolutions都能理解这个协议，因此这使得用户可以访问所有内部变量以及可以通过大量的观看它们的方法。

遗传优化
NeuroSolutions的用户层以及以上层级包含了遗传优化功能。遗传优化功能使得用户可以对神经网络中的任意参数进行优化，以降低出错率。比如，用户可以对隐藏单元的数量，学习率，以及输入选择等进行优化以提高神经网络的性能。

敏感度分析
敏感度分析是一种用于提取神经网络的输入与输出之间的原因以及影响关系的方法。其基本的设计理念是，神经网络的输入通道发生轻微偏移，输出端即可相应的对之进行报告。那些只产生较小的敏感值的输入通道将被视为无关紧要的，因此常常被从神经网络中移除掉，这种操作减小了神经网络的规模，而这也反而减少了网络的复杂性以及所需的训练时间。此外，这还将提高网络对样本数据测试的性能。

样本加权
分类问题中往往每一个类都不可能具有相同数目的训练样本，比如，用户可能拥有一个用于检测临床测试数据中癌症发生概率的神经网络应用程序，该问题的测试数据可能包含了99个分类为非癌症患者的样本，以及一个被标记为癌症患者的样本数据。此时，一个标准化得神经网络将往往将所有的样本分类为非癌症患者，因此其有99%的准确率，而事实上，其目的应该是检测到存在的癌症患者，因此这暴露出了问题。

 NeuroSolutions为用户提供了一种更佳的解决方案，即使用了一种名为加权的方式。以以上例子为例，训练样本中的每一个癌症患者在反向传播中都将拥有比非癌症患者高99倍的权重。这种平衡训练数据的方式使得系统能 以一种更有的方式进行癌症数据的检测。

宏指令
NeuroSolutions拥有一套综合全面的宏语言，这使得用户可以记录操作的顺序，并将之存贮为程序。每一个可以使用鼠标或者键盘进行操作的动作都可以使用一条宏语句操作。这项强大的功能使得用户在构建，编辑和运行神经网络时拥有了前所未有的灵活性。

OLE自动化
lNeuroSolutions是一个完全兼容OLE自动化的服务器。这意味着其可以从OLE自动化控制器中接受控制信息，比如Visual C++, Visual Basic, Microsoft Excel, Microsoft Access, 和Delphi.等

Summary

NeuroSolutions Features

• Input Projection
  Further reduce input dimensions by automatically mapping multiple pieces of information to single inputs.
• Input Optimization
  Automatically determine the most informative inputs through greedy search, back-elimination and other methods.
• CUDA GPU Processing
  NeuroSolutions users can now harness the massive processing power of their NVIDIA graphics cards with the NeuroSolutions CUDA Add-on.
• Faster Processing
  Improved utilization of multi-core processors and optimized executable code results in significantly shorter training times!
• Support Vector Machine Regression
  The Support Vector Machine Regression (SVM-R) .
• Enhanced Probablistic Neural Network Support
• Neuro-Fuzzy
  The coactive neuro-fuzzy inference system (CANFIS) model integrates fuzzy inputs with a neural network to quickly solve poorly defined problems.
• Support Vector Machine
  The Support Vector Machine (SVM) model maps inputs to a high-dimensional feature space, and then optimally separates data into their respective classes by isolating those inputs that fall close to the data boundaries. They are especially effective in separating sets of data that share complex boundaries.
• Levenberg-Marquardt
  This second-order learning algorithm generally trains significantly faster than Momentum learning and usually arrives at a solution with a significantly lower error.
• Teacher Forcing / Iterative Prediction
  There are some time-series prediction problems that are best modeled using a method called teacher forcing. This specialized training algorithm feeds the predicted output back into the input in order to improve the accuracy of multi-step prediction.

Temporal Neural Networks

NeuroSolutions is one of the few neural network development tools to fully support backpropagation through time (BPTT). Instead of mapping a static input to a static output, BPTT maps a series of inputs to a series of outputs. This provides the ability to solve temporal problems by extracting how data changes over time.

User-defined Neural Topologies

NeuroSolutions is based on the concept that neural networks can be broken down into a fundamental set of neural components. Individually these components are relatively simplistic, but several components connected together can result in networks capable of solving very complex problems. The network construction wizards will connect these components for you based on your specifications. However, once the network is built you can arbitrarily change interconnections and/or add in new components. In other words, a virtually infinite number of neural models are possible!

User-defined Neural Components

Every NeuroSolutions component implements a function conforming to a simple protocol in C. To add a new component you simply modify the template function for the base component and compile the code into a DLL -- all directly from NeuroSolutions!

C++ Code Generation

An application developer can integrate a NeuroSolutions neural network into their application by generating a DLL with the Custom Solution Wizard or by generating the C++ source code for the network using the Developers level of NeuroSolutions. The source code generation facility of NeuroSolutions is as robust as its object-oriented design environment. No matter how simple or complex of a network you create within the graphical user interface, NeuroSolutions will generate the equivalent neural network in ANSI C++ source code -- even those networks that contain your own algorithms implemented with DLLs!

Extensive Probing Capabilities

Neural networks are often criticized as being a "black box" technology. With NeuroSolutions' extensive and versatile set of probing tools, this is no longer the case. Probes provide you with real-time access to all internal network variables, such as:

• Inputs/Outputs
• Weights
• Errors
• Hidden States
• Gradients
• Sensitivities

Probing is an important step in the neural network design process, therefore we have made it an integral part of NeuroSolutions. As with the neural components, the probe components are inherently modular; the way you view the data is independent of what the data represents. All network data are reported through a common protocol, and all NeuroSolutions probes understand this protocol. This provides you with access to all internal variables, along with a variety of ways to visualize them.

Genetic Optimization

The Users level of NeuroSolutions and above include Genetic Optimization. Genetic Optimization allows you to optimize virtually any parameter in a neural network to produce the lowest error. For example, the number of hidden units, the learning rates, and the input selection can all be optimized to improve the network performance.

Sensitivity Analysis

Sensitivity analysis is a method for extracting the cause and effect relationship between the inputs and outputs of the network. The basic idea is that each input channel to the network is offset slightly and the corresponding change in the output(s) is reported. The input channels that produce low sensitivity values can be considered insignificant and can most often be removed from the network. This will reduce the size of the network, which in turn reduces the complexity and the training time. Furthermore, this will likely also improve the network performance for the out-of-sample testing data.

Exemplar Weighting

Classification problems often do not have an equal number of training exemplars (samples) for each class. For example, you may have a neural network application that detects the occurrence of cancer from clinical test data. The training data for this problem may contain 99 exemplars classified as non-cancerous for every one exemplar classified as cancerous. A standard neural network would most often train itself to classify all exemplars as non-cancerous so that it would be 99% correct. Since the goal is to detect the existence of cancer, this is a problem.

NeuroSolutions provides a better solution using a method called exemplar weighting. For the example above, each of the cancerous training exemplars would have 99 times more weight during the backpropagation procedure than the non cancerous exemplars. This balancing of the training data will most likely result in a system that does a much better job of detecting the cancerous cases.

Macros

NeuroSolutions has a comprehensive macro language, which allows the user to record a sequence of operations and store them as a program. Any action that can be performed using the mouse and keyboard can be duplicated with a macro statement. This powerful feature gives the user unprecedented flexibility in constructing, editing, and running neural networks.

OLE Automation

NeuroSolutions is a fully compliant OLE Automation Server. This means that NeuroSolutions can receive control messages from OLE Automation Controllers, such as Visual C++, Visual Basic, Microsoft Excel, Microsoft Access, and Delphi.