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Feature Set

Key features

  • Create and apply neural networks to:
    • Forecasting
    • Classification
    • Function Approximation
    • Data Anomalies Detection
  • Analyze and preprocess datasets
  • Automatically search for the best neural network architecture
  • Analyze network performance with graphs and detailed statistics
  • Easy-to-use interface

Analyze and Pre-process Your Data

  • Import Excel files
  • Import popular ASCII file formats (CSV, TXT, PRN)
  • Custom date formats and file structure definition
  • Input dataset size is limited only by the hardware of the computer

  • Date/Time values encoding
  • Categorical values encoding
  • Numeric values scaling
  • Min/max values specification for numeric columns scaling

  • Missing values handling for both numeric and categorical data
  • Outliers handling for numeric data
  • Automatic recognition of data entry errors (wrong type values)
  • Visual representation of data anomalies in the Dataset window

  • Automatic and manual column type identification (numeric, categorical, date, time, text)
  • Random, sequential and manual dataset partition onto training, validation and test sets
  • Accept/ignore records and columns manually

  • Statistical information for data columns
  • Binary columns for anomalies indication
  • Two methods of automatic lag columns insertion
  • Preprocessed data representation
  • Detailed Data Analysis and Data Preprocessing Reports

Design Neural Network

  • Input feature selection (GA, stepwise, exhaustive).
  • Manual architecture specification (up to 5 hidden layers for multi-layer perceptron)
  • Heuristic architecture search with customizable range of search and sensitivity
  • Exhaustive architecture search
  • 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 sets
  • Network visualization

  • Training algorithms: Conjugate Gradient Descent, Levenberg-Marquardt, Quick-Propagation, Quasi-Newton, Quasi-Newton (Limited Memory), Incremental and Batch Back-Propagation
  • Automatic adjustment of learning rate and momentum for Back-Propagation algorithm
  • Activation functions: Linear, Logistic, Tanh, Softmax
  • Error functions: Sum-of-Squares, Cross-entropy
  • Classification model: Winner-takes-all, Confidence-limits (Accept/Reject levels)

Control Network Training Process

  • Real-time training error graph
  • Real-time control on training parameters:
    • errors on training and validation set: MSE, MAE, CCR
    • error improvement
    • training speed (iterations per second)
    • # of iterations.
  • 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
  • Stopping conditions:
    • target error on training and validation sets: MSE, MAE, CCR
    • error improvement: network error, dataset error
    • number of iterations
    • generalization loss
  • Automatic network retrains and selection of the best network among retrains
  • Retrains statistics
  • Weights initialization: manual randomization range; optimized for Uniform or Gaussian distribution

Test and Analyze Performance

  • Actual vs Output graph
  • Scatter plot
  • Response graph
  • Confusion matrix
  • ROC curve
  • Actual vs Output Table with absolute and relative errors
  • Input importance graph

Apply Network

  • Enter new cases manually or insert from the Clipboard
  • Load new cases from a new data file
  • Apply to selected records from your original dataset
  • Graphical network output representation
  • Output representation with Results Table
  • Confidence limits for network output
  • Save results in a separate file or copy them to the Clipboard

General

  • Customizable interface
  • Detailed reporting
  • Online help system
  • Free technical support
  • Project files to keep all related information in one place
  • Sample financial, marketing, real estate and scientific problems included