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