{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T14:30:36Z","timestamp":1779114636174,"version":"3.51.4"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"13","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>This study proposes a nonlinear function-fitting prediction model combining the Newton-CG optimization algorithm with the Transformer architecture to address the limited accuracy and generalization of high-dimensional nonlinear data. Traditional methods often struggle with slow convergence and overfitting when dealing with complex nonlinear relationships. In this paper, the Transformer's multi-head self-attention mechanism is used to capture long-term dependencies in the data, and the Newton-CG method is used to accelerate parameter optimization during model training, thereby significantly improving fitting accuracy and computational efficiency. In the experimental part, three typical nonlinear functions and two public high-dimensional data sets are selected for verification, and the model's average test-set fitting error is reduced to 0.023, which is about 71.5% and 68.2% higher than those of the traditional LSTM and BP network methods. At the same time, the introduction of the Newton-CG method reduces the number of training iterations by about 40% and the average convergence time by 60%. The results show that the proposed model achieves high accuracy and strong generalization in nonlinear function fitting, providing an effective solution to the prediction problem in complex systems.<\/jats:p>","DOI":"10.31449\/inf.v50i13.13648","type":"journal-article","created":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T13:54:28Z","timestamp":1779112468000},"source":"Crossref","is-referenced-by-count":0,"title":["Nonlinear Function Fitting and Prediction Using Newton-CG Optimization and Transformer Architecture"],"prefix":"10.31449","volume":"50","author":[{"given":"Fang","family":"Gao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,5,18]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/13648\/6724","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/13648\/6724","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T13:54:29Z","timestamp":1779112469000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/13648"}},"subtitle":["Enhancing Accuracy and Efficiency in High-Dimensional Nonlinear Predictions"],"short-title":[],"issued":{"date-parts":[[2026,5,18]]},"references-count":0,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2026,5,18]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i13.13648","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5,18]]}}}