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NeuroShell Predictor  類神經網路發展系統

NeuroShell 預測包含先進的演算法,訓練速度極快,使您能夠在最短的時間內有效地解決了預測,預測和估計問題不用通過調整網絡參數的繁瑣過程。該產品的設計是非常容易使用,包含了我們最強大的網絡。 讀取和寫入文字檔中,以便與許多其他程式的相容性。

 

兩個最常聽到的抱怨以前的預測系統,除了太難用,是它們速度太慢,或他們不準確地告訴你是多麼重要。我們已經解決的那些問題。這就是為什麼我們有要從中選擇的兩種培訓模式:從中選擇:

1.第一次的訓練方法,我們稱之為"神經法"基於演算法稱為 Turboprop2,由 Scott Fahlman 發明的卡耐基 · 梅隆大學的著名級聯相關演算法的變體。TurboProp2 動態增長隱藏的神經元和列車速度非常快。TurboProp2 模型的建立 (訓練) 在幾秒鐘內相比更舊的神經網路類型為小時。

 

2.第二種方法,"遺傳訓練法",是由唐納德 · 拜切特發明的廣義回歸神經網路 (廣義) 遺傳演算法變異。它訓練出的樣本模式 ; 中的一切它基本上做一個"一個持有出"技術,也被稱為"刀切法"交叉驗證"。如果使用這種方法訓練時,你基本上看設置出的樣品的培訓。當您不具有許多模式上進行訓練時,此方法是因此極為有效。遺傳訓練方法時間長,培訓更多模式添加到訓練集。

 

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 network parameters. Designed to be extremely easy to use, this product contains our most powerful 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 net 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.

 

Ward Systems Group, Inc.公司產品

AI Trilogy

GeneHunter

NeuroShell Predictor

NeuroShell Classifier

NeuroShell Run-Time Server

NeuroShell 2

NeuroShell Trader, Trader Professional, and DayTrader Professional

NeuroShell Engine

 

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