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Neural Network Software Comparison

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Product Comparison of;

Forecaster: Forecasting tool for MS Excel based on neural networks. It is targeted for Excel users who need a quick-to-learn and reliable forecasting tool embedded into familiar Excel interface.

Neuro Intelligence: Neuro Intelligence is neural network software designed to assist experts in solving real-world problems. Aimed at solution of real-world problems, Neuro Intelligence features only proven algorithms and techniques, is fast and easy-to-use

The three neural networks-based tools are targeted at users with different goals. All designed to solve real-world problems. All share similar interface ideas and proprietary heuristics. To find out what product is right for you use the feature comparison table below.

Feature Forecaster Neuro Intelligence
Excel add-in interface (optimized for MS Excel users)    
Wizard-like interface (different modes for beginners and experts)
Windows tabbed interface (optimized for experts)  
Automatic and manual data analysis and preprocessing
Automatic selection of neural network architecture and training parameters
Online help system
Free technical support
Sample financial, business and scientific problems included
Analyze and Pre-process Your Data
Import popular ASCII file formats (CSV, TXT, PRN)
Import Excel files)
Custom date formats and file structure definition
Automatic data analysis and pre-processing
Automatic categorical values encoding
Automatic numeric values scaling
Automatic Date/Time values encoding
Manual min/max values specification for scaling
Visual representation of data anomalies
Outliers handling for numeric data (customizable outlier coefficient)
Missing values handling for numeric values (removal and 4 substitution options)
Missing values handling for categorical values (removal and 3 substitution options)  
Automatic recognition of data entry errors (wrong type values)
Detailed data analysis and data preprocessing reports
Automatic dataset partition to training, validation and test sets (random or sequential)
Manual dataset partition to training, validation and test sets
Manual column type identification (numeric, categorical, date, time, text)
Accept/ignore records and columns manually
Preprocessed data representation  
Binary columns for anomalies indication  
Two methods of automatic lag columns insertion  
Statistical information for data columns  
Design Neural Network
Input feature selection (GA, stepwise, exhaustive).  
Fully automated neural network design with a constructive algorithm.    
Fully automated neural network design using architecture search heuristics
Manual architecture specification (for multi-layer perceptron)
Customizable heuristic architecture search method  
Three heuristic methods of neural network architecture search.
Exhaustive architecture search with customizable parameters
Customizable search range and search sensitivity  
Detailed statistics for each tested architecture  
Network fitness criteria: AIC, Test set error, Correlation, R-squared  
Graphical representation of network fitness  
Time-series networks  
Network visualization  
Network sets  
Automatic adjustment of learning rate and momentum for Back-Propagation algorithm
Training algorithms: Conjugate Gradient Descent, Levenberg-Marquardt, Quick-Propagation, Incremental and Batch Back-Propagation
Additional training algorithms: Quasi-Newton, Quasi-Newton (Limited Memory)  
Activation functions: Linear, Logistic, Tanh, Softmax  
Error functions: Sum-of-Squares, Cross-entropy  
Classification model: Winner-takes-all, Confidence-limits (Accept/Reject levels)  
Heuristics for automatic generation of stop training conditions
Generalization loss control (10 preset levels)
Retrain network to get better results
Manual stopping conditions (target error level, error improvement, correct classification rate, number of iterations)
Real-time control on training parameters (MSE, MAE, CCR, # of iterations).
Training Error Graph (network error by iteration)
Training Error Table (network error and error improvement by iteration)  
Control Network Training Process
Real-time output of training parameters
Continue training with new parameters  
Jog weights  
Add jitter  
Correlation and r-squared real-time graphs  
Error improvement graph  
Weights distribution graph  
Error distribution graph  
Input importance graph  
Training log: test and validation set error for each iteration  
Early-stopping on generalization loss
Retain and restore best network
Automatic network retrains and selection of the best network among retrains
Manual network retrain
Retrains statistics  
Weights initialization: manual randomization range; optimized for Uniform or Gaussian distribution  
Test and Analyze Performance
Actual vs Forecasted graph  
Actual vs Forecasted scatter plot  
Confusion matrix  
Response graph  
ROC curve  
Actual vs Forecasted table with absolute and relative errors
Tolerance levels to quickly estimate overall forecasting quality    
Input importance graph  
Estimated forecasting error
Apply Network
Enter new cases manually or from the Clipboard
Load new cases from a new data file
Apply to selected records from your original dataset  
Visual output representation with Response Graph  
Output representation with Results Table
Confidence limits for network output  
Save results in a separate file  
Enjoy User Interface Extras
Detailed explanations on every step
Customizable reports (with preview and printing capabilities)
Reports export to HTML and XLS
Save/Load neural network
Two convenient methods of data selection in one interface (by range and by column)    
Complete color customization for reports and graphs    
Neural network auto save    
Price $249 $399
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