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!
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
Calculates the Receiver Operating Characteristic (ROC) curve and the
area under it.
Converts network classification probabilities into binary 0/1
numbers and creates an array of winner indexes
Creates a neural network
Returns a string associated with a specific error code.
Fires the neural network with new input data and produces network
Retrieves the input importance factors for the network currently in
Retrieves the statistics matrix for the Classifier network.
Loads a network into memory from a file on the disk.
Saves a network currently in memory into a file on the disk.
Sets a fitness function penalty matrix for the Classifier Genetic
Performs one cycle of the network training.
Sets/returns the name of a class (category) for a classification
Sets/returns a flag, instructing the control to allow training
continuation (Genetic strategy only).
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
Sets/returns the tolerance value (Predictor/Neural and
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
Sets/returns the enhanced generalization level at which the network
fires new data to produce predictions.
Sets/returns the fitness function mode, which determines how the
Classifier Genetic network is trained.
Returns number of generations, which the network has been trained
without improvement (Genetic training only).
Sets/returns the name of an input variable in the network.
Returns maximum number of hidden neurons, which the Neural type of
network can train (Neural training strategy only).
Returns a special constant value to be used instead of missing input
Returns type of the network currently in the memory.
Returns number of categories (classes) in the classification
Returns number of inputs to the network.
Returns/sets the optimal number of hidden neurons in the network
(Neural training only).
Sets/returns the name of an output variable in the network.
Returns type of the program used to create and train network
currently in the memory.
Ward Systems Group, Inc.公司產品
NeuroShell Run-Time Server
NeuroShell Trader, Trader
Professional, and DayTrader Professional