Open AccessAlgorithms and methods for extracting knowledge about objects defined by arrays of empirical data using ANN models
Author(s) -
А. А. Арзамасцев,
Natalia Zenkova,
N A Kazakov
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1902/1/012097
Subject(s) - computer science , artificial neural network , identification (biology) , parametric statistics , inertia , parametric model , artificial intelligence , channel (broadcasting) , knowledge extraction , data mining , object (grammar) , machine learning , algorithm , mathematics , computer network , statistics , botany , physics , classical mechanics , biology
Most Data Mining techniques are based on traditional methods of constructing mathematical models and their parametric identification. The article considers the technology of extracting knowledge about objects, given by empirical data arrays. It is based on detecting some characteristic features in the object generating these data. They can include dynamic and static characteristics; delays in various channels; inertia of channels or inertia combined with delay; different channel sensitivity; bypass and certain classes of nonlinearities. We also considered the objects that are characterized by a constant flow of new data, which makes it necessary to retrain the model. As a tool for obtaining knowledge in this work, the apparatus of artificial neural networks (ANNs) is used. It includes a structure builder, which allows you to quickly change the structure of ANN models and machine training algorithms. This enables you to successfully solve parametric identification problems. Algorithms and methods of knowledge extraction have been developed for these tasks. Examples that confirm the possibility of their use are given.