NeuroSolutions
Level Summary
|
|
A |
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B |
|
C |
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|
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|
|
Topologies |
Multilayer Perceptron (MLP) |
|
Y |
|
Y |
|
Y |
Generalized Feedforward Network |
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Y |
|
Y |
|
Y |
Modular Network |
|
Y |
|
Y |
|
Y |
Jordan / Elman Networks |
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Y |
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Y |
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Y |
Self-Organizing Map (SOM) |
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Y |
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Y |
|
Y |
Principal Component Analysis (PCA) |
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Y |
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Y |
|
Y |
Radial Basis Function (RBF) |
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Y |
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Y |
|
Y |
Probabilistic Neural Network (PNN) |
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Y |
|
Y |
|
Y |
General Regression Neural Network (GRNN) |
|
Y |
|
Y |
|
Y |
Neuro-Fuzzy Network (CANFIS) |
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Y |
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Y |
|
Y |
Support Vector Machine Network |
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Y |
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Y |
|
Y |
Hopfield Network |
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|
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Y |
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Y |
Time Delay Neural Network (TDNN) |
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Y |
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Y |
Time-Lag Recurrent Network (TLRN) |
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Y |
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Y |
General Recurrent Network |
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Y |
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Y |
Maximum Number of Inputs / Outputs / Neurons Per Layer |
|
500 |
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Unlimited |
Maximum Number of Hidden Layers |
|
6 |
|
Unlimited |
Learning Paradigms |
Backpropagation |
|
Y |
|
Y |
|
Y |
Unsupervised Learning (Hebbian,Ojas,Sangers,Competitive,Kohonen) |
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Y |
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Y |
|
Y |
Recurrent Backpropagation |
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Y |
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Y |
Backpropagation through time |
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Y |
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Y |
Gradient Descent Methods |
Step / Momentum |
|
Y |
|
Y |
|
Y |
Delta Bar Delta |
|
Y |
|
Y |
|
Y |
Quickprop |
|
Y |
|
Y |
|
Y |
Conjugate Gradient |
|
Y |
|
Y |
|
Y |
Levenberg-Marquardt |
|
Y |
|
Y |
|
Y |
Advanced Features |
Exemplar Weighting - Improved training for data with unequal class distribution |
|
Y |
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Y |
|
Y |
Macros / OLE Automation - API to Automate and control NeuroSolutions |
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Y |
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Y |
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Y |
Sensitivity Analysis - to determine the most influential inputs |
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Y |
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Y |
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Y |
Genetic Optimization - Intelligent searching for the best parameters and inputs |
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Y |
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Y |
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Y |
Iterative Prediction - Advanced method for time series prediction |
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Y |
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Y |
ANSI C++ Source Code Generation - Embedded neural networks into your own applications, train neural networks on faster computers |
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Y |
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Y |
User-defined Neural Components (using DLLs) (Nonlinearities,Interconnections, Learning Rules, Error Criteria, input/Output, Memory Structures |
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Y |