• Home
  • Products & Solutions
    • By Business Domain
      • Active Traders
      • Brokers
      • Energy
      • Retail
      • Healthcare
      • Insurance
      • Science
    • By Solution
      • NeuroIntelligence
      • Forecaster XL
      • NeuroFusion
      • Tradecision
      • Forecaster
      • NeuroDienst
      • BrokerBOOSTER
      • iBrokerAge
    • Customers
    • Testimonials
  • Services
    • Overview
    • Outsourced Product Development
    • Expertise
    • Experience
    • Business Models
    • Product Delivery
  • Purchase
  • Support
    • Support Centre
    • Priority Annual Support
    • Support Request
    • Request a Feature
  • Company
    • Company Info
    • Mission
    • Consulting
    • Technology
    • Research
    • Contact Us
   

Products & Solutions / By Solution / Neurointelligence / Feature Set

   
By Business Domain
Active Traders
Brokers
Energy
Retail
Healthcare
Insurance
Science
By Solution
NeuroIntelligence
Product Info
Take a Tour
Key Features
Feature Set
System Requirements
Forecaster XL
NeuroFusion
Tradecision
Forecaster
NeuroDienst
BrokerBOOSTER
iBrokerAge
Customers
Testimonials
For customer acquisition, risk mitigation, customer management and fraud prevention solutions we recommend our strategic partner - Scorto Corp.
  • Decision Management
  • Loan Origination Software
  • Customer Management
  • Debt Collection Software
  • Fraud Detection Systems
  • SME Loan Management

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

© 2001-2012 Alyuda Research, LLC. All rights reserved.
Use of this website signifies your agreement to the Terms of Use.
Privacy Policy | Contact us | Site map
Ask Us a Question
  |  
Request a Quote
Ask Us a Question
Close window
*Indicates a required field
Name*:
E-mail*:
Company*:
Please enter your message here*:
  
Request a Quote
Close window
*Indicates a required field
Name*:
E-mail*:
Company*:

Select the item you are interested in:

Please enter your message here*: