NeuroShell Predictor集成了前沿的预测算法

，可能实现极速训练

，让用户无需繁琐调整neural网络参数即可高阶解决预测、预报和估算问题
。作为一款易用的产品

，它蕴含了壮大的neural网络引擎

，并支持文本文件读写以实现与其他法式的兼容
。

该预测算法经过多年钻研

，彻底握别了必要人为调整数十个参数来构建优质模型且预防过拟合的时期

，也无需再礼聘neural网络专家或统计学家来成立预测模型
。

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针对传统预测系统普遍存在的两大痛点——使用复杂性和运行效能低下

，以及无法正确评估变量沉要性

，NeuroShell Predictor提供了甄选的解决规划
。为此

，提供两种训练模式选择：

1. "Neural网络步骤"：基于卡内基梅隆大学Scott Fahlman发现的Cascade Correlation算法改进的Turboprop2算法
   
   ，可动态天生暗藏neure并实现秒级训练
   
   ，相比传统neural网络需数幼时的训练功夫拥有显著优势
   。

2. "遗传训练步骤"：选取Donald Specht提出的广义回归neural网络(GRNN)的遗传算法变体
   
   ，全程选取样本表训练模式
   
   ，内容上使用了"留一法"(也称为"刀切法"或"交叉验证")
   。当训练样本有限时
   
   ，这种步骤尤其有效
   
   ，但随着样本量增长训练功夫会相应耽搁
   。

两种步骤均提供自变量(输入变量)沉要性分析职能

，援手用户鉴别模型中的关键变量
。

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NeuroShell Predictor的易用性使其险些无需说明书！内置的"领导系统"会全程疏导用户实现预测模型构建

，每个步骤都配有详尽的援手文档
。纯熟后

，用户可关关领导系统

，直接使用工具栏或菜单运行
。法式还提供可打印的在线高低文有关参考手册
。

对于必要将neural网络模型嵌入自有法式或进行分发的用户

，我们提供可选的运行时服务器(Run-Time Server)

，模型分发无需支付版税或其他用度
。

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NeuroShell Predictor支持从电子表格导入数据并以数据网格大局展示：

? 可拔取陆续或随机数据行作为训练集和样本表测试集

?

? 可从数据文件列中自由选择输入变量和输出指标

?

? 可矫捷选择neural网络或遗传训练步骤

?

? Neural网络步骤仅需单一设置

，彻底握别传统反向传布算法繁琐的参数调整

?

? 遗传训练步骤提供三种现代优化技术和多种优化指标选择

?

? 训练实现后可对训练数据或样本表数据利用neural网络模型

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产品还蕴含高阶的3D图形分析插件

，用于进行活络度分析：
? 表汇预测模型的输入变量分析图

?

? 销售预测的输入身分分析图

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【英文介绍】

The NeuroShell Predictor contains state-of-the-art algorithms that train extremely fast, enabling you to effectively solve prediction, forecasting, and estimation problems in a minimum amount of time without going through the tedious process of tweaking neural network parameters. Designed to be extremely easy to use, this product contains our most powerful neural networks. Reads and writes text files for compatibility with many other programs.

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The prediction algorithms are the crowning achievement of several years of research. Gone are the days of dozens of parameters that must be artistically set to create a good model without over-fitting. Gone are the days of hiring a neural network expert or a statistician to build your predictive models.

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Two of the most commonly heard complaints about previous prediction systems, aside from being too hard to use, are that they are too slow or that they do not accurately tell you how important each of the variables is to the model. We've taken care of those problems. That's why we have two training models from which to choose:

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1. The first training method, which we call the “neural method” is based on an algorithm called Turboprop2, a variant of the famous Cascade Correlation algorithm invented at Carnegie Mellon University by Scott Fahlman. TurboProp2 dynamically grows hidden neurons and trains very fast.? TurboProp2 models are built (trained) in a matter of seconds compared to hours for older neural networks types.? ?

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2. The second method, the “genetic training method”, is a genetic algorithm variation of the General regression neural network (GRNN) invented by Donald Specht. It trains everything in an out-of-sample mode; it is essentially doing a "one-hold-out" technique, also called "jackknife" or "cross validation".? If you train using this method, you are essentially looking at the training set out-of-sample.? This method is therefore extremely effective when you do not have many patterns on which to train. The genetic training method takes longer to train as more patterns are added to the training set.

Both training methods provide an analysis of independent variables (inputs) to help you determine which ones are most important in your model.

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?The NeuroShell Predictor is so easy to use that it doesn't need a manual! Instead, there is an "Instructor" that guides you through making the predictive models. At every stage of the Instructor, our extensive help file will give you all the information you need. When you have learned from the Instructor, you can turn it off and work from the toolbar or menus.? The program does include an on-line, context sensitive reference manual that you may print yourself or just browse from your computer.

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Finally, for those who want to embed the resulting neural models into your own programs, or to distribute the results, there is an optional Run-Time Server available. Predictor models may be distributed without incurring royalties or other fees.

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