乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,31]],"date-time":"2024-08-31T07:30:30Z","timestamp":1725089430391},"reference-count":73,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2020,10,16]],"date-time":"2020-10-16T00:00:00Z","timestamp":1602806400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,10,16]],"date-time":"2020-10-16T00:00:00Z","timestamp":1602806400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2021,6]]},"DOI":"10.1007\/s00521-020-05427-z","type":"journal-article","created":{"date-parts":[[2020,10,16]],"date-time":"2020-10-16T19:02:36Z","timestamp":1602874956000},"page":"6627-6640","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A new hybrid model to foretell thermal power efficiency from energy performance certificates at residential dwellings applying a Gaussian process regression"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-8880-6348","authenticated-orcid":false,"given":"Paulino Jos\u00e9","family":"Garc\u00eda-Nieto","sequence":"first","affiliation":[]},{"given":"Esperanza","family":"Garc\u00eda-Gonzalo","sequence":"additional","affiliation":[]},{"given":"Jos\u00e9 Pablo","family":"Paredes-S\u00e1nchez","sequence":"additional","affiliation":[]},{"given":"Antonio","family":"Bernardo S\u00e1nchez","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,16]]},"reference":[{"key":"5427_CR1","doi-asserted-by":"crossref","first-page":"101533","DOI":"10.1016\/j.scs.2019.101533","volume":"48","author":"M Bourdeau","year":"2019","unstructured":"Bourdeau M, Zhai XQ, Nefzaoui E, Guo X, Chatellier P (2019) Modelling and forecasting building energy consumption: a review of data-driven techniques. Sustain Cities Soc 48:101533","journal-title":"Sustain Cities Soc"},{"key":"5427_CR2","doi-asserted-by":"crossref","first-page":"1272","DOI":"10.1016\/j.rser.2015.12.040","volume":"56","author":"VSKV Harish","year":"2016","unstructured":"Harish VSKV, Kumar A (2016) A review on modeling and simulation of building energy systems. Renew Sust Energ Rev 56:1272\u20131292","journal-title":"Renew Sust Energ Rev"},{"key":"5427_CR3","unstructured":"European Commission (2002) Directive 2002\/91\/EC of the European parliament and of the council of 16 December 2002 on the energy performance of buildings, Official Journal of the European Communities"},{"key":"5427_CR4","doi-asserted-by":"crossref","first-page":"2728","DOI":"10.3390\/en13112728","volume":"13","author":"BM Paredes-S\u00e1nchez","year":"2020","unstructured":"Paredes-S\u00e1nchez BM, Paredes-S\u00e1nchez JP, Garc\u00eda Nieto PJ (2020) Energy multiphase model for biocoal conversion systems by means of a nodal network. Energies 13:2728\u20132740","journal-title":"Energies"},{"key":"5427_CR5","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1016\/j.jclepro.2018.05.170","volume":"194","author":"JP Paredes-S\u00e1nchez","year":"2018","unstructured":"Paredes-S\u00e1nchez JP, Conde M, G\u00f3mez MA, Alves D (2018) Modelling hybrid thermal systems for district heating: a pilot project in wood transformation industry. J Clean Prod 194:726\u2013734","journal-title":"J Clean Prod"},{"key":"5427_CR6","doi-asserted-by":"crossref","first-page":"109244","DOI":"10.1016\/j.rser.2019.109244","volume":"113","author":"Y Li","year":"2019","unstructured":"Li Y, Kubicki S, Guerriero A, Rezgui Y (2019) Review of building energy performance certification schemes towards future improvement. Renew Sust Energ Rev 113:109244","journal-title":"Renew Sust Energ Rev"},{"key":"5427_CR7","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.enbuild.2016.04.067","volume":"125","author":"F Khayatian","year":"2016","unstructured":"Khayatian F, Sarto L, Dall\u2019O\u2019 G (2016) Application of neural networks for evaluating energy performance certificates of residential buildings. Energy Build 125:45\u201354","journal-title":"Energy Build"},{"key":"5427_CR8","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.jclepro.2014.08.071","volume":"109","author":"H Son","year":"2015","unstructured":"Son H, Kim C (2015) Early prediction of the performance of green building projects using pre-project planning variables: data mining approaches. J Clean Prod 109:144\u2013151","journal-title":"J Clean Prod"},{"key":"5427_CR9","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.enbuild.2016.09.033","volume":"131","author":"AP Melo","year":"2016","unstructured":"Melo AP, Versage RS, Sawaya G, Lamberts R (2016) A novel surrogate model to support building energy labelling system: a new approach to assess cooling energy demands in commercial buildings. Energy Build 131:233\u2013247","journal-title":"Energy Build"},{"key":"5427_CR10","doi-asserted-by":"crossref","DOI":"10.1201\/9780429402296","volume-title":"Building performance simulation for design and operation","author":"JLM Hensen","year":"2019","unstructured":"Hensen JLM, Lamberts R (2019) Building performance simulation for design and operation. Routledge, New York"},{"key":"5427_CR11","doi-asserted-by":"crossref","DOI":"10.1002\/9781119341901","volume-title":"Building performance analysis","author":"P de Wilde","year":"2018","unstructured":"de Wilde P (2018) Building performance analysis. Wiley-Blackwell, New York"},{"key":"5427_CR12","volume-title":"Gaussian processes in machine learning: summer school on machine learning","author":"CE Rasmussen","year":"2003","unstructured":"Rasmussen CE (2003) Gaussian processes in machine learning: summer school on machine learning. Springer, Berlin"},{"key":"5427_CR13","unstructured":"Ebden M (2015) Gaussian processes: a quick introduction. https:\/\/arxiv.org\/pdf\/1505.02965.pdf. Accessed 27 May 2020"},{"key":"5427_CR14","volume-title":"Gaussian processes, function theory, and the inverse spectral problem","author":"H Dym","year":"2008","unstructured":"Dym H, McKean HP (2008) Gaussian processes, function theory, and the inverse spectral problem. Dover, New York"},{"key":"5427_CR15","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1023\/A:1008202821328","volume":"11","author":"R Storn","year":"1997","unstructured":"Storn R, Price K (1997) Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11:341\u2013359","journal-title":"J Global Optim"},{"key":"5427_CR16","volume-title":"Differential evolution: a practical approach to global optimization","author":"K Price","year":"2005","unstructured":"Price K, Storn RM, Lampinen JA (2005) Differential evolution: a practical approach to global optimization. Springer, Berlin"},{"key":"5427_CR17","volume-title":"Differential evolution: in search of solutions","author":"V Feoktistov","year":"2006","unstructured":"Feoktistov V (2006) Differential evolution: in search of solutions. Springer, New York"},{"issue":"1","key":"5427_CR18","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/MAP.2011.5773566","volume":"53","author":"P Rocca","year":"2011","unstructured":"Rocca P, Oliveri G, Massa A (2011) Differential evolution as applied to electromagnetics. IEEE Antennas Propag 53(1):38\u201349","journal-title":"IEEE Antennas Propag"},{"key":"5427_CR19","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4471-0577-0","volume-title":"Genetic algorithms: concepts and designs","author":"K-F Man","year":"1999","unstructured":"Man K-F, Tang K-S, Kwong S (1999) Genetic algorithms: concepts and designs. Springer, New York"},{"key":"5427_CR20","volume-title":"Practical genetic algorithms","author":"RL Haupt","year":"2004","unstructured":"Haupt RL, Haupt SE (2004) Practical genetic algorithms. Wiley-Interscience, New York"},{"key":"5427_CR21","volume-title":"Genetic algorithms in search, optimization and machine learning","author":"DE Goldberg","year":"2008","unstructured":"Goldberg DE (2008) Genetic algorithms in search, optimization and machine learning. Dorling Kindersley Pvt Ltd, London"},{"key":"5427_CR22","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-662-44874-8","volume-title":"Introduction to evolutionary computing","author":"AE Eiben","year":"2015","unstructured":"Eiben AE, Smith JE (2015) Introduction to evolutionary computing. Springer, New York"},{"key":"5427_CR23","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-52156-5","volume-title":"Genetic algorithm essentials","author":"O Kramer","year":"2017","unstructured":"Kramer O (2017) Genetic algorithm essentials. Springer, Berlin"},{"issue":"11","key":"5427_CR24","doi-asserted-by":"crossref","first-page":"1613","DOI":"10.1002\/nme.1620141104","volume":"14","author":"H Matthies","year":"1979","unstructured":"Matthies H, Strang G (1979) The solution of nonlinear finite element equations. Int J Numer Meth Eng 14(11):1613\u20131626","journal-title":"Int J Numer Meth Eng"},{"issue":"151","key":"5427_CR25","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1090\/S0025-5718-1980-0572855-7","volume":"35","author":"J Nocedal","year":"1980","unstructured":"Nocedal J (1980) Updating quasi-Newton matrices with limited storage. Math Comput 35(151):773\u2013782","journal-title":"Math Comput"},{"key":"5427_CR26","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1007\/BF01589116","volume":"45","author":"DC Liu","year":"1989","unstructured":"Liu DC, Nocedal J (1989) On the limited memory BFGS method for large scale optimization. Math Program 45:503\u2013528","journal-title":"Math Program"},{"key":"5427_CR27","doi-asserted-by":"crossref","first-page":"1190","DOI":"10.1137\/0916069","volume":"16","author":"RH Byrd","year":"1994","unstructured":"Byrd RH, Lu P, Nocedal J, Zhu C (1994) A limited-memory algorithm for bound constrained optimization. SIAM J Sci Comp 16:1190\u20131208","journal-title":"SIAM J Sci Comp"},{"issue":"4","key":"5427_CR28","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1145\/279232.279236","volume":"23","author":"C Zhu","year":"1997","unstructured":"Zhu C, Byrd RH, Lu P, Nocedal J (1997) Algorithm 778: L-BFGS\u2013B: Fortran subroutines for large-scale bound-constrained optimization. ACM T Math Software 23(4):550\u2013560","journal-title":"ACM T Math Software"},{"key":"5427_CR29","volume-title":"Engineering optimization: theory and practice","author":"SS Rao","year":"2009","unstructured":"Rao SS (2009) Engineering optimization: theory and practice. Wiley, New York"},{"key":"5427_CR30","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-91578-4","volume-title":"Lectures on convex optimization","author":"Y Nesterov","year":"2018","unstructured":"Nesterov Y (2018) Lectures on convex optimization. Springer, Berlin"},{"key":"5427_CR31","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/3206.001.0001","volume-title":"Gaussian processes for machine learning","author":"CE Rasmussen","year":"2005","unstructured":"Rasmussen CE, Williams CKI (2005) Gaussian processes for machine learning. The MIT Press, Cambridge"},{"key":"5427_CR32","doi-asserted-by":"crossref","first-page":"108497","DOI":"10.1016\/j.ijheatfluidflow.2019.108497","volume":"80","author":"Y Duan","year":"2019","unstructured":"Duan Y, Cooling C, Ahn JS, Jackson C, Flint A, Eaton MD, Bluck MJ (2019) Using a Gaussian process regression inspired method to measure agreement between the experiment and CFD simulations. Int J Heat Fluid Fl 80:108497","journal-title":"Int J Heat Fluid Fl"},{"key":"5427_CR33","doi-asserted-by":"crossref","first-page":"105950","DOI":"10.1016\/j.optlaseng.2019.105950","volume":"127","author":"S Wang","year":"2020","unstructured":"Wang S, Zhu L, Fuh JYH, Zhang H, Yan W (2020) Multi-physics modeling and Gaussian process regression analysis of cladding track geometry for direct energy deposition. Opt Laser Eng 127:105950","journal-title":"Opt Laser Eng"},{"key":"5427_CR34","doi-asserted-by":"crossref","first-page":"109406","DOI":"10.1016\/j.enbuild.2019.109406","volume":"202","author":"YG Akhlaghi","year":"2019","unstructured":"Akhlaghi YG, Zhao X, Shittu S, Badiei A, Cattaneo MEGV, Ma X (2019) Statistical investigation of a dehumidification system performance using Gaussian process regression. Energ Buildings 202:109406","journal-title":"Energ Buildings"},{"key":"5427_CR35","doi-asserted-by":"crossref","first-page":"107256","DOI":"10.1016\/j.apacoust.2020.107256","volume":"164","author":"AS Alghamdi","year":"2020","unstructured":"Alghamdi AS, Polat K, Alghoson A, Alshdadi AA, Abd El-Latif AA (2020) Gaussian process regression (GPR) based non-invasive continuous blood pressure prediction method from cuff oscillometric signals. Appl Acoust 164:107256","journal-title":"Appl Acoust"},{"key":"5427_CR36","doi-asserted-by":"crossref","first-page":"116467","DOI":"10.1016\/j.energy.2019.116467","volume":"190","author":"X Li","year":"2020","unstructured":"Li X, Yuan C, Li X, Wang Z (2020) State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression. Energy 190:116467","journal-title":"Energy"},{"key":"5427_CR37","doi-asserted-by":"crossref","first-page":"101054","DOI":"10.1016\/j.jobe.2019.101054","volume":"28","author":"A Zeng","year":"2020","unstructured":"Zeng A, Ho H, Yu Y (2020) Prediction of building electricity usage using Gaussian Process Regression. J Build Eng 28:101054","journal-title":"J Build Eng"},{"key":"5427_CR38","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.sigpro.2019.02.011","volume":"160","author":"L Ambrogioni","year":"2019","unstructured":"Ambrogioni L, Maris E (2019) Complex-valued Gaussian process regression for time series analysis. Signal Process 160:215\u2013228","journal-title":"Signal Process"},{"key":"5427_CR39","doi-asserted-by":"crossref","first-page":"2112","DOI":"10.1016\/j.renene.2019.08.018","volume":"146","author":"H Cai","year":"2020","unstructured":"Cai H, Jia X, Feng J, Li W, Hsu Y, Lee J (2020) Gaussian Process Regression for numerical wind speed prediction enhancement. Renew Energ 146:2112\u20132123","journal-title":"Renew Energ"},{"key":"5427_CR40","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.actaastro.2018.12.020","volume":"157","author":"A Gao","year":"2019","unstructured":"Gao A, Liao W (2019) Efficient gravity field modeling method for small bodies based on Gaussian process regression. Acta Astronaut 157:73\u201391","journal-title":"Acta Astronaut"},{"issue":"1","key":"5427_CR41","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1016\/j.asr.2019.11.011","volume":"65","author":"IG Gon\u00e7alves","year":"2020","unstructured":"Gon\u00e7alves IG, Echer E, Frigo E (2020) Sunspot cycle prediction using warped Gaussian process regression. Adv Space Res 65(1):677\u2013683","journal-title":"Adv Space Res"},{"key":"5427_CR42","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1016\/j.renene.2016.05.083","volume":"97","author":"D Sarkar","year":"2016","unstructured":"Sarkar D, Contal E, Vayatis N, Dias F (2016) Prediction and optimization of wave energy converter arrays using a machine learning approach. Renew Energ 97:504\u2013517","journal-title":"Renew Energ"},{"key":"5427_CR43","doi-asserted-by":"crossref","first-page":"109298","DOI":"10.1016\/j.jcp.2020.109298","volume":"408","author":"J Zhang","year":"2020","unstructured":"Zhang J, Taflanidis AA, Scruggs JT (2020) Surrogate modeling of hydrodynamic forces between multiple floating bodies through a hierarchical interaction decomposition. J Comput Phys 408:109298","journal-title":"J Comput Phys"},{"key":"5427_CR44","doi-asserted-by":"crossref","first-page":"3586","DOI":"10.1016\/j.rser.2012.02.049","volume":"16","author":"H Zhao","year":"2012","unstructured":"Zhao H, Magoul\u00e8s F (2012) A review on the prediction of building energy consumption. Renew Sustain Energy Rev 16:3586\u20133592","journal-title":"Renew Sustain Energy Rev"},{"key":"5427_CR45","doi-asserted-by":"crossref","first-page":"102010","DOI":"10.1016\/j.scs.2019.102010","volume":"54","author":"T Ahmad","year":"2020","unstructured":"Ahmad T, Chen H (2020) A review on machine learning forecasting growth trends and their real-time applications in different energy systems. Sustain Cities Soc 54:102010","journal-title":"Sustain Cities Soc"},{"key":"5427_CR46","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.enbuild.2015.02.052","volume":"94","author":"RZ Jovanovic","year":"2015","unstructured":"Jovanovic RZ, Sretenovic AA, Zivkovic BD (2015) Ensemble of various neural networks for prediction of heating energy consumption. Energy Build 94:189\u2013199","journal-title":"Energy Build"},{"key":"5427_CR47","doi-asserted-by":"crossref","first-page":"1719","DOI":"10.1016\/j.apenergy.2019.04.087","volume":"250","author":"M Rampazzo","year":"2019","unstructured":"Rampazzo M, Lionello M, Beghi A, Sisti E, Cecchinato L (2019) A static moving boundary modelling approach for simulation of indirect evaporative free cooling systems. Appl Energ 250:1719\u20131728","journal-title":"Appl Energ"},{"key":"5427_CR48","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1016\/j.enbuild.2014.07.036","volume":"82","author":"JS Chou","year":"2014","unstructured":"Chou JS, Bui DK (2014) Modeling heating and cooling loads by artificial intelligence for energy-efficient building design. Eng Build 82:437\u2013446","journal-title":"Eng Build"},{"key":"5427_CR49","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.rser.2014.01.069","volume":"33","author":"AS Ahmad","year":"2014","unstructured":"Ahmad AS, Hassan MY, Abdullah MP, Rahman HA, Hussin F, Abdullah H, Saidur R (2014) A review on applications of ANN and SVM for building electrical energy consumption forecasting. Renew Sustain Energy Rev 33:102\u2013109","journal-title":"Renew Sustain Energy Rev"},{"key":"5427_CR50","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.apenergy.2006.09.012","volume":"85","author":"M Aydinalp-Koksal","year":"2008","unstructured":"Aydinalp-Koksal M, Ugursal VI (2008) Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector. Appl Eng 85:271\u2013296","journal-title":"Appl Eng"},{"key":"5427_CR51","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.1016\/j.rser.2017.04.095","volume":"81","author":"K Amasyali","year":"2018","unstructured":"Amasyali K, El-Gohary NM (2018) A review of data-driven building energy consumption prediction studies. Renew Sustain Eng Rev 81:1192\u20131205","journal-title":"Renew Sustain Eng Rev"},{"key":"5427_CR52","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.enbuild.2018.03.042","volume":"168","author":"YR Yoon","year":"2018","unstructured":"Yoon YR, Moon HJ (2018) Energy consumption model with energy use factors of tenants in commercial buildings using Gaussian process regression. Energy Build 168:215\u2013224","journal-title":"Energy Build"},{"key":"5427_CR53","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.enbuild.2016.02.004","volume":"119","author":"FM Gray","year":"2016","unstructured":"Gray FM, Schmidt M (2016) Thermal building modelling using Gaussian processes. Energy Build 119:119\u2013128","journal-title":"Energy Build"},{"key":"5427_CR54","doi-asserted-by":"crossref","first-page":"109899","DOI":"10.1016\/j.enbuild.2020.109899","volume":"215","author":"RF Mustapa","year":"2020","unstructured":"Mustapa RF, Dahlan NY, Yassin AIM, Nordin AHM (2020) Quantification of energy savings from an awareness program using NARX-ANN in an educational building. Energy Build 215:109899","journal-title":"Energy Build"},{"key":"5427_CR55","unstructured":"Asturian Energy Foundation (FAEN) (2020) Technical report. http:\/\/www.faen.es\/ceee\/estadisticas\/ceee_estadisticas_municipios.html. Accessed 29 May 2020"},{"key":"5427_CR56","unstructured":"Spanish Institute for Diversification and Energy Saving (IDAE) (2019) Technical software. https:\/\/energia.gob.es\/desarrollo\/EficienciaEnergetica\/CertificacionEnergetica\/DocumentosReconocidos\/Paginas\/procedimientos-certificacion-proyecto-terminados.aspx. Accessed 26 May 2020"},{"issue":"1","key":"5427_CR57","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/S0951-8339(96)00017-2","volume":"10","author":"I Rychlik","year":"1997","unstructured":"Rychlik I, Johannesson P, Leadbetter MR (1997) Modelling and Statistical Analysis of ocean-wave data using transformed Gaussian processes. Mar Struct 10(1):13\u201347","journal-title":"Mar Struct"},{"key":"5427_CR58","volume-title":"Pattern recognition and machine learning","author":"CM Bishop","year":"2011","unstructured":"Bishop CM (2011) Pattern recognition and machine learning. Springer, New York"},{"key":"5427_CR59","doi-asserted-by":"crossref","first-page":"106816","DOI":"10.1016\/j.ress.2020.106816","volume":"197","author":"M Li","year":"2020","unstructured":"Li M, Sadoughi M, Hu Z, Hu C (2020) A hybrid Gaussian process model for system reliability analysis. Reliab Eng Syst Safe 197:106816","journal-title":"Reliab Eng Syst Safe"},{"key":"5427_CR60","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.jprocont.2019.06.007","volume":"81","author":"A Daemi","year":"2019","unstructured":"Daemi A, Kodamana H, Huang B (2019) Gaussian process modelling with Gaussian mixture likelihood. J Process Contr 81:209\u2013220","journal-title":"J Process Contr"},{"key":"5427_CR61","volume-title":"MATLAB for machine learning","author":"G Ciaburro","year":"2017","unstructured":"Ciaburro G (2017) MATLAB for machine learning. Packt Publishing, Birmingham"},{"key":"5427_CR62","volume-title":"Machine learning with R: expert techniques for predictive modeling","author":"B Lantz","year":"2019","unstructured":"Lantz B (2019) Machine learning with R: expert techniques for predictive modeling. Packt Publishing, Birmingham"},{"key":"5427_CR63","volume-title":"Evolutionary optimization algorithms","author":"D Simon","year":"2013","unstructured":"Simon D (2013) Evolutionary optimization algorithms. Wiley, New York"},{"key":"5427_CR64","volume-title":"Swarm intelligence and bio-inspired computation: theory and applications","author":"X Yang","year":"2013","unstructured":"Yang X, Cui Z, Xiao R, Gandomi AH, Karamanoglu M (2013) Swarm intelligence and bio-inspired computation: theory and applications. Elsevier, London"},{"key":"5427_CR65","unstructured":"Liu J, Lampinen J (2002) On setting the control parameter of the differential evolution method. In: Proceedings of the 8th international conference on soft computing, MENDEL, Brno, Czech Republic, pp 11\u201318"},{"key":"5427_CR66","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-33946-7","volume-title":"Adaptive regression for modeling nonlinear relationships","author":"GJ Knafl","year":"2016","unstructured":"Knafl GJ, Ding K (2016) Adaptive regression for modeling nonlinear relationships. Springer, Berlin"},{"key":"5427_CR67","volume-title":"Statistics","author":"JT McClave","year":"2016","unstructured":"McClave JT, Sincich TT (2016) Statistics. Pearson, New York"},{"key":"5427_CR68","unstructured":"GPy (2014) A Gaussian process framework in python. http:\/\/github.com\/SheffieldML\/GPy. Accessed 25 May 2014"},{"key":"5427_CR69","volume-title":"Bayes\u2019 rule with python: a tutorial introduction to Bayesian analysis","author":"JV Stone","year":"2016","unstructured":"Stone JV (2016) Bayes\u2019 rule with python: a tutorial introduction to Bayesian analysis. Sebtel Press, London"},{"key":"5427_CR70","unstructured":"Seeger M (2000) Bayesian model selection for support vector machines, Gaussian processes and other kernel classifiers. In: NIPS\u201999 Proceedings of the 12th International Conference on Neural Information Processing Systems, MIT Press, Cambridge, MA, USA, vol. 12, pp 603\u2013609"},{"key":"5427_CR71","first-page":"1","volume-title":"IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP)","author":"J Piironen","year":"2016","unstructured":"Piironen J, Vehtari A (2016) Projection predictive model selection for Gaussian processes. IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE Publisher, Vietri sul Mare, pp 1\u20136"},{"key":"5427_CR72","unstructured":"Paananen T, Piironen J, Andersen MR, Vehtari A (2019) Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution. In: Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), Proceedings of Machine Learning Research (PMLR), Naha, Okinawa, Japan, pp 1743\u20131752"},{"key":"5427_CR73","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.resconrec.2017.10.020","volume":"129","author":"H Ye","year":"2018","unstructured":"Ye H, Ren Q, Hu X, Lin T, Shi L, Zhang G, Li X (2018) Modeling energy-related CO2 emissions from office buildings using general regression neural network. Resour Conserv Recy 129:168\u2013174","journal-title":"Resour Conserv Recy"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05427-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05427-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05427-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,16]],"date-time":"2021-10-16T01:28:14Z","timestamp":1634347694000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05427-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,16]]},"references-count":73,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2021,6]]}},"alternative-id":["5427"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05427-z","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,16]]},"assertion":[{"value":"6 August 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 October 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 October 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}