DADiSP / NeuralNets
Neural Network Module
Neural networks are able to recognize underlying patterns and
predict outcomes based on incomplete or inconsistent information.
In contrast to expert systems, which use a static set of rules,
neural networks are able to learn and adapt to changes in the
environment. With DADiSP/NeuralNet, users can build their own
artificial neural networks (ANNs) and apply them to achieve more
accurate predictions and pattern classifications.
Neural Network Training
Neural networks resemble the human brain because they can learn. A
back-propagation neural network develops its predictive
capabilities by being trained on a set of historical inputs and
known resulting outputs. The neural net applies random weights to
each designated input variable. It then adjusts the weights
depending on how closely the actual output values match the desired
output values in the training set of historical data. Once the
appropriate variable weights have been set that minimize the
difference between expected and actual output from the neural net,
the neural net can then be applied to new data for classification.
Back-propagation Learning Algorithm
DADiSP/NeuralNet employs the back-propagation learning algorithm.
Back-propagation has become the most widely used neural network
paradigm for modeling, forecasting, and classification. To
minimize the error in the network, DADiSP/NeuralNet uses a
rapid-descent algorithm derived from the Vogl method of locating
the global minimum. Since results depend on the initial
conditions, the neural net module allows you to train a lot of
neural networks on the same data with different initial
configurations and pick the best one.
Powerful Preprocessing Functions
Preprocessing of the data is one of the largest problems in using
neural network tools. DADiSP/NeuralNet is fully integrated with
DADiSP, so hundreds of analysis functions are available to pre- and
post-process neural network data. DADiSP has mathematical and
statistical functions to scale, filter, and process the data to
identify features to be learned by the neural network. A typical
Worksheet will contain the preprocessing steps, the neural network
and the output results. Simply change the input data or initial
conditions and each dependent Window is automatically recalculated.
You immediately see the effects of your changes on the neural
DADiSP/NeuralNet requires DADiSP 6.0 B18
for information about updating your current
version of DADiSP.