The new product is called the NeuroShell Engine. It is an Active X control that can be loaded into your Visual Basic, C++, or C# programs to bring the neural network power of the NeuroShell Predictor and Classifier to your programs. Incorporating the best neural networks on the planet is a piece of cake!

I. Background
The NeuroShell Engine is a product that most of our users will never need, because our other programs allow you to call your networks from computer programs you write. Such computer programs can be distributed throughout your company and to your customers. If you purchase the NeuroShell Predictor or NeuroShell Classifier, you simply have to purchase the NeuroShell Runtime Server in order to place calls to your nets into your Windows programs written in Visual Basic, C++, Delphi, and other languages. If you purchase NeuroShell 2, it includes a runtime server so you can call your programs from the aforementioned languages in Windows, and you can also generate source code so your nets can be compiled on mainframes or Unix workstations.

Use of neural nets involves two stages:
Stage 1, TRAINING. In this stage, the historic or sample observations are collected to build the neural model. The process is called training, where the net "learns" the sample observations. The end result is a model that can be used in stage 2.

Stage 2, FIRING (executing). In this stage, the model built in stage 1 is called (or "fired") to produce answers for new input data, data that was normally not used in stage 1 to build the model. This is the production environment where you utilize the model to make predictions, classifications, or other decisions.

Of course, our programs (NeuroShell Predictor, NeuroShell Classifier and NeuroShell 2) provide both stage 1 and stage 2 in them in graphical user interfaces. However, only stage 2 can be controlled from within your own computer programs written by your own programmers. (NeuroShell 2 includes this capability; NeuroShell Predictor and Classifier require the Runtime Server.)

Stage 1, in all of our other products, is accomplished only from a graphical user interface that is built into our products. That's where the NeuroShell Engine comes in. The Engine lets you call stage 1 (training) from your computer programs. In essence, it allows you to build your own neural network products for distribution! You can build custom designed neural network product interfaces too, by dropping our engine into your chassis.

II. What is the NeuroShell Engine
The NeuroShell Engine is only for the most serious neural network users, and only those who are programmers or have programmers on staff. The NeuroShell Engine contains the neural and genetic training methods that we have used ourselves in the NeuroShell Predictor, the NeuroShell Classifier, and the NeuroShell Trader. They are available to be integrated into your own computer programs for both training and firing neural networks. Basically there are four algorithms:

1. the neural method (Turboprop 2) used in the Predictor and Trader
2. The genetic method (Advanced GRNN) used in the Predictor
3. The neural method (Turboprop 2) used in the Classifier
4. The genetic method (Advanced PNN) used in the Classifier

III. Technical Details
If you are not a programmer, you will probably not understand the following description. The NeuroShell Engine is an Active X control which can be utilized in modern compilers like Microsoft Visual Basic and Microsoft Visual C++. The control can be added to a program just as other controls are added, like combo boxes and text boxes. Once the control is added to a program, the programmer can create "network" objects that can be trained and fired. Network objects have properties and methods. The properties allow access to parameters of the networks you build, like the number of inputs, maximum number of hidden neurons, etc. The methods facilitate training and firing of the network. All of these things are accessed and manipulated from within your programs.

The picture below shows the NeuroShell Engine control after it has been added to a Visual Basic Program:

Methods and Properties
The NeuroShell Engine allows you to set the following methods and properties:
　

Methods

CalcROCcurve
Calculates the Receiver Operating Characteristic (ROC) curve and the area under it.

Convert2Binary
Converts network classification probabilities into binary 0/1 numbers and creates an array of winner indexes

CreateNet
Creates a neural network

ErrMessage
Returns a string associated with a specific error code.

FireNet
Fires the neural network with new input data and produces network predictions.

GetInputsImportance
Retrieves the input importance factors for the network currently in the memory.

GetMatrix
Retrieves the statistics matrix for the Classifier network.

LoadNet
Loads a network into memory from a file on the disk.

SaveNet
Saves a network currently in memory into a file on the disk.

SetFitnessMatrix
Sets a fitness function penalty matrix for the Classifier Genetic network.

TrainNet
Performs one cycle of the network training.

Properties
ClassNames
Sets/returns the name of a class (category) for a classification network.

ContinueTraining
Sets/returns a flag, instructing the control to allow training continuation (Genetic strategy only).

GeneticPenalty
Sets/returns the genetic penalty flag to minimize the number of unpredictable patterns (Predictor/Genetic only). This property is identical to the “Minimize number of unpredictable patterns” check box found on the “Advanced Genetic” tab under the Options menu in NeuroShell Predictor.

InRangeTolerance
Sets/returns the tolerance value (Predictor/Neural and Predictor/Genetic only).

TighterOptimization
Sets/returns tighter optimization flag for the best curve fitting (Predictor/Genetic only). This property is identical to the “Favor tighter fitting during optimization” check box found on the “Advanced Genetic” tab under the Options menu in NeuroShell Predictor.

EnhancedGeneralization
Sets/returns the enhanced generalization level at which the network fires new data to produce predictions.

FitnessFunctionMode
Sets/returns the fitness function mode, which determines how the Classifier Genetic network is trained.

GenerationsWithNoChange
Returns number of generations, which the network has been trained without improvement (Genetic training only).

InNames
Sets/returns the name of an input variable in the network.

MaxNumHiddens
Returns maximum number of hidden neurons, which the Neural type of network can train (Neural training strategy only).

MissingValue
Returns a special constant value to be used instead of missing input data.

NetType
Returns type of the network currently in the memory.

NumCategories
Returns number of categories (classes) in the classification network.

NumInputs
Returns number of inputs to the network.

OptimalHiddens
Returns/sets the optimal number of hidden neurons in the network (Neural training only).

OutNames
Sets/returns the name of an output variable in the network.

ProgramType
Returns type of the program used to create and train network currently in the memory.

　

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