NeuroShell Predictor - 神经网络软件

NeuroShell Predictor集成了前沿的预测算法，能够实现极速训练，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，它包含了强大的neural网络引擎，并支持文本文件读写以实现与其他程序的兼容。

该预测算法经过多年研究，彻底告别了需要人工调整数十个参数来构建优质模型且避免过拟合的时代，也无需再聘请neural网络专家或统计学家来建立预测模型。

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率低下，以及无法准确评估变量重要性，NeuroShell Predictor提供了甄选的解决方案。为此，提供两种训练模式选择：

1. "Neural网络方法"：基于卡内基梅隆大学Scott Fahlman发明的Cascade Correlation算法改进的Turboprop2算法，可动态生成隐藏neure并实现秒级训练，相比传统neural网络需数小时的训练时间具有显著优势。

2. "遗传训练方法"：采用Donald Specht提出的广义回归neural网络(GRNN)的遗传算法变体，全程采用样本外训练模式，实质上运用了"留一法"(也称为"刀切法"或"交叉验证")。当训练样本有限时，这种方法尤其有效，但随着样本量增加训练时间会相应延长。

两种方法均提供自变量(输入变量)重要性分析功能，帮助用户识别模型中的关键变量。

NeuroShell Predictor的易用性使其几乎无需说明书！内置的"指导系统"会全程引导用户完成预测模型构建，每个步骤都配有详尽的帮助文档。熟练后，用户可关闭指导系统，直接使用工具栏或菜单运行。程序还提供可打印的在线上下文相关参考手册。

对于需要将neural网络模型嵌入自有程序或进行分发的用户，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费用。

NeuroShell Predictor支持从电子表格导入数据并以数据网格形式展示：

• 可选取连续或随机数据行作为训练集和样本外测试集

• 可从数据文件列中自由选择输入变量和输出目标

• 可灵活选择neural网络或遗传训练方法

• Neural网络方法仅需简单设置，彻底告别传统反向传播算法繁琐的参数调整

• 遗传训练方法提供三种现代优化技术和多种优化目标选择

• 训练完成后可对训练数据或样本外数据应用neural网络模型

产品还包含高阶的3D图形分析插件，用于进行灵敏度分析：
• 外汇预测模型的输入变量分析图

• 销售预测的输入要素分析图

【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.

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.

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:

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.   

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.

 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.

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.