Stipulator and Brainer

Data analysis and machine learning tools for signal classification and time series prediction


  • Comprehensive development environment using neural networks and models
  • Combine, analyze, and convert signals from various data sources and formats
  • Process signals and extract key features for signal conversion to formats for machine learning tools
  • Comprehensive pre- and postprocessing utility for Neural Network Toolbox™
  • Export models to embedded code
  • Generate reports


Sensor signal classification and time series prediction is a common task that can be carried out well using machine learning methods.

Stipulator and Brainer constitute a comprehensive development suite for example-based requirements definition, training, analysis, and validation of signal classification and time series prediction models.

Example-based requirements definition consists of three aspects:

  • Which signals and signal processing to use
  • When and how to classify them
  • Which cases or examples to consider

Data from various sources can be imported, collected, arranged, and processed within Stipulator. Stipulator can also be used for general analysis and plotting of sensor signals, as well as for detection and visualization of potential classification conflicts. Finally, Stipulator processes the signal data and extracts signal features to formats for arbitrary machine learning tools, such as Brainer and Neural Network ToolboxTM.

Brainer is a comprehensive, graphical pre- and postprocessing tool for Neural Network Toolbox, which is specialized for, but not limited to, time series classification and regression. In addition to Neural Network Toolbox, other machine learning models are interfaced and integrated, such as the decision and regression trees of Statistics and Machine Learning Toolbox™. In addition to training, evaluation, and common postprocessing functions of the models, several feature selection algorithms are implemented, such as backward-forward selection, oscillating search, evolutionary feature selection, etc. These methods allow for a quick and effective identification of signal features that facilitate the given classification or regression task.

Technically, Stipulator and Brainer are two complementary toolboxes. Nevertheless, both can run as stand-alone toolboxes as well. Both tools can be extensively customized, allowing users to integrate their own functions, models, signal processing, performance evaluations, etc.

Andata Development Technologies

Hallburgstraße 5
Hallein, Österreich 5400
Tel: +43-6245-74063
Fax: +43-6245-74063 20

Required Products


  • Linux
  • Windows


  • E-mail
  • Telephone

Product Type

  • Data Analysis Tools


  • Control Systems
  • Data Analysis and Statistics
  • Digital Signal Processing
  • System Modeling and Simulation


  • Aerospace and Defense
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  • Communication Infrastructure
  • Financial Services
  • Industrial Automation and Machinery
  • Rail, Ships, and Other Transportation