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For highly non-linear problems with insufficient data set, the prediction accuracy of NN models becomes unstable, resulting in a decrease in the accuracy of ensembles. Therefore, this study proposes a prediction frequency-based ensemble that identifies core prediction values, which are core prediction members to be used in the ensemble and are expected to be concentrated near the true response. The prediction frequency-based ensemble classifies core prediction values \u200b\u200bsupported by multiple NN models \u200b\u200bby conducting statistical analysis with a frequency distribution, which is a collection of prediction values \u200b\u200bobtained from various NN models for a given prediction point. The prediction frequency-based ensemble searches for a range of prediction values that contains prediction values above a certain frequency, and thus the predictive performance can be improved by excluding prediction values with low accuracy \u200b\u200band coping with the uncertainty of the most frequent value. An adaptive sampling strategy that sequentially adds samples based on the core prediction variance calculated as the variance of the core prediction values is proposed to improve the predictive performance of the prediction frequency-based ensemble efficiently. Results of various case studies show that the prediction accuracy of the prediction frequency-based ensemble is higher than that of Kriging and other existing ensemble methods. In addition, the proposed adaptive sampling strategy effectively improves the predictive performance of the prediction frequency-based ensemble compared with the previously developed space-filling and prediction variance-based strategies.<\/jats:p>","DOI":"10.1093\/jcde\/qwad071","type":"journal-article","created":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T00:38:22Z","timestamp":1689122302000},"page":"1547-1560","source":"Crossref","is-referenced-by-count":6,"title":["Adaptive neural network ensemble using prediction frequency"],"prefix":"10.1093","volume":"10","author":[{"given":"Ungki","family":"Lee","sequence":"first","affiliation":[{"name":"Ground Technology Research Institute, Agency for Defense Development , Bugyuseong-daero 488-160, Yuseong-gu, Daejeon 34060 , Republic of Korea"}]},{"given":"Namwoo","family":"Kang","sequence":"additional","affiliation":[{"name":"Cho Chun Shik Graduate School of Mobility, Korea Advanced Institute of Science and Technology , Daejeon 34051 , Republic of Korea"}]}],"member":"286","published-online":{"date-parts":[[2023,7,11]]},"reference":[{"key":"2023071809553085000_bib1","doi-asserted-by":"crossref","first-page":"e00938","DOI":"10.1016\/j.heliyon.2018.e00938","article-title":"State-of-the-art in artificial neural network applications: A survey","volume":"4","author":"Abiodun","year":"2018","journal-title":"Heliyon"},{"key":"2023071809553085000_bib2","doi-asserted-by":"crossref","first-page":"3668","DOI":"10.1109\/JSTARS.2014.2331255","article-title":"Mapping of the solar irradiance in the UAE using advanced artificial neural network ensemble","volume":"7","author":"Alobaidi","year":"2014","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"article-title":"Altair HyperStudy tutorials","year":"2017","author":"Altair","key":"2023071809553085000_bib3"},{"key":"2023071809553085000_bib4","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1016\/j.jhydrol.2019.05.066","article-title":"An ensemble neural network model for real-time prediction of urban floods","volume":"575","author":"Berkhahn","year":"2019","journal-title":"Journal of Hydrology"},{"key":"2023071809553085000_bib5","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780198538493.001.0001","volume-title":"Neural Networks for Pattern Recognition","author":"Bishop","year":"1995"},{"key":"2023071809553085000_bib6","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1007\/s00158-020-02488-5","article-title":"Scalable gradient\u2013enhanced artificial neural networks for airfoil shape design in the subsonic and transonic regimes","volume":"61","author":"Bouhlel","year":"2020","journal-title":"Structural and Multidisciplinary Optimization"},{"key":"2023071809553085000_bib7","doi-asserted-by":"crossref","first-page":"3127","DOI":"10.1007\/s00158-020-02659-4","article-title":"The heat source layout optimization using deep learning surrogate modelling","volume":"62","author":"Chen","year":"2020","journal-title":"Structural and Multidisciplinary Optimization"},{"key":"2023071809553085000_bib8","doi-asserted-by":"crossref","first-page":"3742","DOI":"10.1016\/j.cma.2008.02.026","article-title":"Reliability analysis of structures using artificial neural network based genetic algorithms","volume":"197","author":"Cheng","year":"2008","journal-title":"Computer Methods in Applied Mechanics and Engineering"},{"key":"2023071809553085000_bib9","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.ijforecast.2011.04.001","article-title":"Advances in forecasting with neural networks? 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