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NeuroSolutions
 
 
   
Level Summary
Topologies              
Multilayer Perceptron (MLP)  
Generalized Feedforward Network  
Modular Network    
Jordan / Elman Networks    
Self-Organizing Map (SOM)    
Principal Component Analysis (PCA)    
Radial Basis Function (RBF)    
Probabilistic Neural Network (PNN)    
General Regression Neural Network (GRNN)    
Neuro-Fuzzy Network (CANFIS)    
Support Vector Machine Network    
Hopfield Network      
Time Delay Neural Network (TDNN)      
Time-Lag Recurrent Network (TLRN)      
General Recurrent Network      
Maximum Number of Inputs / Outputs / Neurons Per Layer 50 500 Unlimited
Maximum Number of Hidden Layers   2 6 Unlimited
Learning Paradigms              
Backpropagation  
Unsupervised Learning    
 
 
 
 
 
   • Hebbian              • Ojas               • Sangers  
   • Competitive        • Kohonen  
Recurrent Backpropagation      
Backpropagation through time      
Gradient Descent Methods              
Step / Momentum  
Delta Bar Delta  
Quickprop  
Conjugate Gradient  
Levenberg-Marquardt  
Advanced Features              
Exemplar Weighting                Ü Improved training for data with unequal class distribution
Macros / OLE Automation      Ü API to Automate and control NeuroSolutions
Sensitivity Analysis                  Ü Technique to determine the most influential inputs
Genetic Optimization              Ü Intelligent searching for the best parameters and inputs  
Iterative Prediction                  Ü Advanced method for time series prediction    
ANSI C++ Source Code Generation        
 
 
 
   • Embed neural networks into your own applications  
   • Train neural networks on faster computers  
User-defined Neural Components (using DLLs)          
 
 
   • Nonlinearities            • Interconnections              • Learning Rules
   • Error Criteria             • Input/Output                     • Memory Structures

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NeuroSolutions overview in Chinese.