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:
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.
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