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An Integral Software Solution of the SGTM Neural-Like Structures Implementation for Solving Different Data Mining Tasks

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Lecture Notes in Computational Intelligence and Decision Making (ISDMCI 2021)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 77))

Abstract

The paper presents a developed software solution that implements a new learning model and application of artificial neural networks, i.e. the Successive Geometric Transformations Model, to solve various applied data mining tasks. This model is applied to construct Feed Forward Neural Networks, which anticipates the rejection of training goal interpretation as a multi-extremal optimization procedure. The modes of use of the system developed (prediction, time series, and cascade modes) are described in detail and illustrated, as well as the structure and functions of separate constituents of the graphical user interface; data selection and download procedures; neuro-like structure parameterization capabilities for each different mode of operation of the program. Simulation of the developed integral software solution was performed while fulfilling prediction, classification, and time series forecasting. Besides, the feasibility of performing nonlinear PCA analysis at high speed is shown. When performing all the above tasks, the program outcomes are presented in both numerical form and using the appropriate one for a particular mode of visualization in 2D or 3D formats.

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Author information

Authors and Affiliations

  1. Lviv Polytechnic National University, Lviv, Ukraine

    Roman Tkachenko

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  1. Roman Tkachenko
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Editors and Affiliations

  1. Department of Physics, Kherson State University, Kherson, Ukraine

    Sergii Babichev

  2. Department of Informatics and Computer Science, Kherson National Technical University, Kherson, Ukraine

    Volodymyr Lytvynenko

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Tkachenko, R. (2022). An Integral Software Solution of the SGTM Neural-Like Structures Implementation for Solving Different Data Mining Tasks. In: Babichev, S., Lytvynenko, V. (eds) Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 77. Springer, Cham. https://doi.org/10.1007/978-3-030-82014-5_48

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