DADiSP / NeuralNet

Neural Network Module

DADiSP/NeuralNet is an add-on module to DADiSP that provides direct and easy access to the demonstrated predictive power and pattern recognition capability of neural networking technology. With DADiSP/NeuralNet, users can build their own artificial neural networks (ANNs) and apply them to achieve more accurate predictions and pattern classifications.

Key Features

• Menu-driven Network Design
• Automatic Normalization of Data
• Choice of the Number of Hidden Layers
• Unlimited Input and Output Variables
• Unlimited Number of Runs
• Cross-validation Training to Verify Output Results Simultaneously
• Built-in Protection Against Local Minima Distorting Output Results
• User Selectable Desired Mean Square Error, Minimum Gradient Norm and Desired Absolute Error
• Digital Error, Analog Error, Maximum Error and Gradient Values Post-Training Error Graph Types
• Extract Random Seeds Gives the Values Used to Start a Network and Enables Building another Network with the Same Weights
• Extract Network Weights Returns the Weights and Biases that Define the Network