Open AccessNonlinear Prediction Models in Data Analysis
Author(s) -
Željko V. Račić,
Zoran Ž. Avramović,
Đuro Mikić
Publication year - 2020
Publication title -
journal of information technology and applications
Language(s) - English
Resource type - Journals
eISSN - 2233-0194
pISSN - 2232-9625
DOI - 10.7251/jit2002106r
Subject(s) - sensibility , artificial neural network , nonlinear system , computer science , function (biology) , artificial intelligence , data mining , value (mathematics) , machine learning , data science , art , quantum mechanics , evolutionary biology , biology , physics , literature
The modern entrepreneurial sensibility of the company’s business implies directing the right information to the appropriate parts of the company at the right time. That is why it is necessary to digitalize processes as much as possible and make the organization “intelligent”, and its human resources, to the greatest extent, the knowledge workers. The application of neural networks, i.e. nonlinear prediction models, enables systematic analysis of data in the function of evaluating the behavior of the system. Neural networks are a powerful tool, especially for forecasting trends and forecasting based on historical data. The grouping method, i.e., the k-mean value algorithm, is used as a precursor to neural networks.