{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T01:04:19Z","timestamp":1767920659945,"version":"3.49.0"},"reference-count":34,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T00:00:00Z","timestamp":1734307200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research Committee of the University of Patras","award":["81845"],"award-info":[{"award-number":["81845"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Mathematical models are designed to assist decision-making processes across various scientific fields. These models typically contain numerous parameters, the values\u2019 estimation of which often comes under analysis when evaluating the strength of these models as management tools. Advanced artificial intelligence software has proven to be highly effective in estimating these parameters. In this research work, we use the Lotka\u2013Volterra model to describe the dynamics of a telecommunication sector in Greece, and then we propose a methodology that employs a feed-forward neural network (NN). The NN is used to estimate the parameter\u2019s values of the Lotka\u2013Volterra system, which are later applied to solve the system using a fourth-algebraic-order Runge\u2013Kutta method. The application of the proposed architecture to the specific case study reveals that the model fits well to the experiential data. Furthermore, the results of our method surpassed the other three methods used for comparison, demonstrating its higher accuracy and effectiveness. The implementation of the proposed feed-forward neural network and the fourth-algebraic-order Runge\u2013Kutta method was accomplished using MATLAB.<\/jats:p>","DOI":"10.3390\/info15120809","type":"journal-article","created":{"date-parts":[[2024,12,17]],"date-time":"2024-12-17T05:26:02Z","timestamp":1734413162000},"page":"809","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Neural Network-Based Parameter Estimation in Dynamical Systems"],"prefix":"10.3390","volume":"15","author":[{"given":"Dimitris","family":"Kastoris","sequence":"first","affiliation":[{"name":"Department of Management Science and Technology, University of Patras, 26504 Patras, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5989-6313","authenticated-orcid":false,"given":"Kostas","family":"Giotopoulos","sequence":"additional","affiliation":[{"name":"Department of Management Science and Technology, University of Patras, 26504 Patras, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0725-3954","authenticated-orcid":false,"given":"Dimitris","family":"Papadopoulos","sequence":"additional","affiliation":[{"name":"Department of Management Science and Technology, University of Patras, 26504 Patras, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Giotopoulos, K.C., Michalopoulos, D., Vonitsanos, G., Papadopoulos, D., Giannoukou, I., and Sioutas, S. 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