{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T16:44:18Z","timestamp":1775753058377,"version":"3.50.1"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2020,1,31]],"date-time":"2020-01-31T00:00:00Z","timestamp":1580428800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,31]],"date-time":"2020-01-31T00:00:00Z","timestamp":1580428800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2020,6]]},"DOI":"10.1007\/s11063-020-10195-x","type":"journal-article","created":{"date-parts":[[2020,1,31]],"date-time":"2020-01-31T14:03:00Z","timestamp":1580479380000},"page":"2301-2316","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Efficient Strategies of Static Features Incorporation into the Recurrent Neural Network"],"prefix":"10.1007","volume":"51","author":[{"given":"Grzegorz","family":"Miebs","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ma\u0142gorzata","family":"Mochol-Grzelak","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adam","family":"Karaszewski","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6940-9432","authenticated-orcid":false,"given":"Rafa\u0142 A.","family":"Bachorz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,1,31]]},"reference":[{"key":"10195_CR1","doi-asserted-by":"publisher","unstructured":"Bengio Y, Frasconi P, Simard P (1993) The problem of learning long-term dependencies in recurrent networks. In: IEEE international conference on neural networks, IEEE. https:\/\/doi.org\/10.1109\/ICNN.1993.298725, https:\/\/ieeexplore.ieee.org\/document\/298725","DOI":"10.1109\/ICNN.1993.298725"},{"issue":"1","key":"10195_CR2","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45(1):5\u201332. https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Mach Learn"},{"key":"10195_CR3","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2019.2930662","author":"D Chen","year":"2019","unstructured":"Chen D, Li S, Lin FJ (2019) New super-twisting zeroing neural-dynamics model for tracking control of parallel robots: a finite-time and robust solution. IEEE Trans Cybern. https:\/\/doi.org\/10.1109\/TCYB.2019.2930662","journal-title":"IEEE Trans Cybern"},{"issue":"1","key":"10195_CR4","doi-asserted-by":"publisher","first-page":"74","DOI":"10.3390\/s19010074","volume":"19","author":"D Chen","year":"2019","unstructured":"Chen D, Li S, Lin FJ, Wu Q (2019b) Rejecting chaotic disturbances using a super-exponential-zeroing neurodynamic approach for synchronization of chaotic sensor systems. IEEE Trans Cybern 19(1):74. https:\/\/doi.org\/10.3390\/s19010074","journal-title":"IEEE Trans Cybern"},{"key":"10195_CR5","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2930685","author":"D Chen","year":"2019","unstructured":"Chen D, Li S, Wu Q, Luo X (2019) New disturbance rejection constraint for redundant robot manipulators: an optimization perspective. IEEE Trans Ind Inf. https:\/\/doi.org\/10.1109\/TII.2019.2930685","journal-title":"IEEE Trans Ind Inf"},{"key":"10195_CR6","first-page":"821","volume-title":"Short-term load forecasting using random forests","author":"G Dudek","year":"2015","unstructured":"Dudek G (2015) Short-term load forecasting using random forests, vol 323. Springer, Berlin, pp 821\u2013828"},{"key":"10195_CR7","first-page":"284","volume-title":"Learning the long-term structure of the blues","author":"D Eck","year":"2002","unstructured":"Eck D, Schmidhuber J (2002) Learning the long-term structure of the blues, vol 2415. Springer, Berlin, pp 284\u2013289"},{"key":"10195_CR8","doi-asserted-by":"crossref","unstructured":"Esteban C, Staeck O, Yang Y, Tresp V (2016) Predicting clinical events by combining static and dynamic information using recurrent neural networks. arXiv: 1602.02685 [cs], arXiv:1602.02685","DOI":"10.1109\/ICHI.2016.16"},{"key":"10195_CR9","doi-asserted-by":"publisher","unstructured":"Feilat EA, Bouzguenda M (2011) Medium-term load forecasting using neural network approach. In: 2011 IEEE PES conference on innovative smart grid technologies\u2014Middle East, IEEE, pp 1\u20135. https:\/\/doi.org\/10.1109\/ISGT-MidEast.2011.6220810, http:\/\/ieeexplore.ieee.org\/document\/6220810\/","DOI":"10.1109\/ISGT-MidEast.2011.6220810"},{"issue":"10","key":"10195_CR10","doi-asserted-by":"publisher","first-page":"2451","DOI":"10.1162\/089976600300015015","volume":"12","author":"FA Gers","year":"2000","unstructured":"Gers FA, Schmidhuber J, Cummins F (2000) Learning to forget: continual prediction with LSTM. Neural Comput 12(10):2451\u20132471. https:\/\/doi.org\/10.1162\/089976600300015015","journal-title":"Neural Comput"},{"issue":"11","key":"10195_CR11","doi-asserted-by":"publisher","first-page":"3135","DOI":"10.1016\/j.enconman.2008.06.004","volume":"49","author":"E Gonz\u00e1lez-Romera","year":"2008","unstructured":"Gonz\u00e1lez-Romera E, Jaramillo-Mor\u00e1n M, Carmona-Fern\u00e1ndez D (2008) Monthly electric energy demand forecasting with neural networks and fourier series. Energy Convers Manag 49(11):3135\u20133142. https:\/\/doi.org\/10.1016\/j.enconman.2008.06.004","journal-title":"Energy Convers Manag"},{"key":"10195_CR12","unstructured":"Graves A, Fern\u00e1ndez S, Liwicki M, Bunke H, Schmidhuber J (2007) Unconstrained online handwriting recognition with recurrent neural networks. In: NIPS\u201907 Proceedings of the 20th international conference on neural information processing systems, Curran Associates Inc., https:\/\/dl.acm.org\/citation.cfm?id=2981562.2981635"},{"key":"10195_CR13","doi-asserted-by":"crossref","unstructured":"Graves A, Mohamed A, Hinton GE (2013) Speech recognition with deep recurrent neural networks. In: 2013 IEEE international conference on acoustics, speech and signal processing, pp 6645\u20136649","DOI":"10.1109\/ICASSP.2013.6638947"},{"issue":"8","key":"10195_CR14","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"key":"10195_CR15","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1016\/j.ijepes.2014.12.036","volume":"67","author":"F Kaytez","year":"2015","unstructured":"Kaytez F, Taplamacioglu MC, Cam E, Hardalac F (2015) Forecasting electricity consumption: a comparison of regression analysis, neural networks and least squares support vector machines. Int J Electr Power Energy Syst 67:431\u2013438. https:\/\/doi.org\/10.1016\/j.ijepes.2014.12.036","journal-title":"Int J Electr Power Energy Syst"},{"issue":"1","key":"10195_CR16","doi-asserted-by":"publisher","first-page":"57","DOI":"10.3390\/en9010057","volume":"9","author":"H Khosravani","year":"2016","unstructured":"Khosravani H, Castilla M, Berenguel M, Ruano A, Ferreira P (2016) A comparison of energy consumption prediction models based on neural networks of a bioclimatic building. Energies 9(1):57. https:\/\/doi.org\/10.3390\/en9010057","journal-title":"Energies"},{"issue":"1","key":"10195_CR17","doi-asserted-by":"publisher","first-page":"213","DOI":"10.3390\/en11010213","volume":"11","author":"PH Kuo","year":"2018","unstructured":"Kuo PH, Huang CJ (2018) A high precision artificial neural networks model for short-term energy load forecasting. Energies 11(1):213. https:\/\/doi.org\/10.3390\/en11010213","journal-title":"Energies"},{"key":"10195_CR18","doi-asserted-by":"publisher","unstructured":"Leontjeva A, Kuzovkin I (2016) Combining static and dynamic features for multivariate sequence classification. In: 2016 IEEE international conference on data science and advanced analytics (DSAA) pp. 21\u201330. https:\/\/doi.org\/10.1109\/DSAA.2016.10, arXiv: 1712.08160","DOI":"10.1109\/DSAA.2016.10"},{"issue":"10","key":"10195_CR19","doi-asserted-by":"publisher","first-page":"827","DOI":"10.3390\/en9100827","volume":"9","author":"Y Liang","year":"2016","unstructured":"Liang Y, Niu D, Ye M, Hong WC (2016) Short-term load forecasting based on wavelet transform and least squares support vector machine optimized by improved cuckoo search. Energies 9(10):827. https:\/\/doi.org\/10.3390\/en9100827","journal-title":"Energies"},{"key":"10195_CR20","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1016\/j.apenergy.2014.05.023","volume":"129","author":"N Liu","year":"2014","unstructured":"Liu N, Tang Q, Zhang J, Fan W, Liu J (2014) A hybrid forecasting model with parameter optimization for short-term load forecasting of micro-grids. Appl Energy 129:336\u2013345. https:\/\/doi.org\/10.1016\/j.apenergy.2014.05.023","journal-title":"Appl Energy"},{"key":"10195_CR21","doi-asserted-by":"publisher","unstructured":"Mayer H, Gome F, Wierstra D, Nagy I, Knoll A, Schmidhuber J (2006) A system for robotic heart surgery that learns to tie knots using recurrent neural networks. In: 2006 IEEE\/RSJ international conference on intelligent robots and systems. https:\/\/doi.org\/10.1109\/IROS.2006.282190","DOI":"10.1109\/IROS.2006.282190"},{"issue":"3","key":"10195_CR22","doi-asserted-by":"publisher","first-page":"408","DOI":"10.3390\/en10030408","volume":"10","author":"D Niu","year":"2017","unstructured":"Niu D, Dai S (2017) A short-term load forecasting model with a modified particle swarm optimization algorithm and least squares support vector machine based on the denoising method of empirical mode decomposition and grey relational analysis. Energies 10(3):408. https:\/\/doi.org\/10.3390\/en10030408","journal-title":"Energies"},{"issue":"6","key":"10195_CR23","doi-asserted-by":"publisher","first-page":"1822","DOI":"10.1016\/j.asoc.2011.07.001","volume":"12","author":"D Niu","year":"2012","unstructured":"Niu D, Shi H, Wu DD (2012) Short-term load forecasting using bayesian neural networks learned by hybrid Monte Carlo algorithm. Appl Soft Comput 12(6):1822\u20131827. https:\/\/doi.org\/10.1016\/j.asoc.2011.07.001","journal-title":"Appl Soft Comput"},{"issue":"1","key":"10195_CR24","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1023\/A:1012046824237","volume":"31","author":"JV Ringwood","year":"2001","unstructured":"Ringwood JV, Bofelli D, Murray FT (2001) Forecasting electricity demand on short, medium and long time scales using neural networks. J Intell Rob Syst 31(1):129\u2013147. https:\/\/doi.org\/10.1023\/A:1012046824237","journal-title":"J Intell Rob Syst"},{"key":"10195_CR25","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323:533\u2013536. https:\/\/doi.org\/10.1038\/323533a0","journal-title":"Nature"},{"key":"10195_CR26","unstructured":"Salehinejad H, Sankar S, Barfett J, Colak E, Valaee S (2018) Recent advances in recurrent neural networks. arXiv:1801.01078"},{"key":"10195_CR27","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.asoc.2013.12.001","volume":"16","author":"A Selakov","year":"2014","unstructured":"Selakov A, Cvijetinovi\u0107 D, Milovi\u0107 L, Mellon S, Bekut D (2014) Hybrid pso-svm method for short-term load forecasting during periods with significant temperature variations in city of burbank. Appl Soft Comput 16:80\u201388. https:\/\/doi.org\/10.1016\/j.asoc.2013.12.001","journal-title":"Appl Soft Comput"},{"key":"10195_CR28","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1016\/j.rser.2015.04.037","volume":"48","author":"L Suganthi","year":"2015","unstructured":"Suganthi L, Iniyan S, Samuel AA (2015) Applications of fuzzy logic in renewable energy systems\u2014a review. Renew Sustain Energy Rev 48:585\u2013607. https:\/\/doi.org\/10.1016\/j.rser.2015.04.037","journal-title":"Renew Sustain Energy Rev"},{"issue":"3","key":"10195_CR29","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1017\/S1351324918000098","volume":"24","author":"M Tanti","year":"2018","unstructured":"Tanti M, Gatt A, Camilleri KP (2018) Where to put the image in an image caption generator. Nat Lang Eng 24(3):467\u2013489. https:\/\/doi.org\/10.1017\/S1351324918000098","journal-title":"Nat Lang Eng"},{"key":"10195_CR30","unstructured":"Vinyals O, Toshev A, Bengio S, Erhan D (2014) Show and tell: a neural image caption generator. arXiv:1411.4555"},{"key":"10195_CR31","unstructured":"Wu Y, Schuster M, Chen Z, Le QW, Norouzi M, Macherey W, Krikun M, Cao Y, Gao Q, Macherey K, Klingner J, Shah A, Johnson M, Liu X, Kaiser L, Gouws S, Kato Y, Kudo T, Kazawa H, Stevens K, Kurian G, Patil N, Wang W, Young C, Smith J, Riesa J, Rudnick A, Vinyals O, Corrado GS, Hughes M, Dean J (2016) Google\u2019s neural machine translation system: Bridging the gap between human and machine translation. arXiv:1609.08144"},{"key":"10195_CR32","doi-asserted-by":"publisher","unstructured":"Yang Y, Fasching PA, Tresp V (2017) Predictive modeling of therapy decisions in metastatic breast cancer with recurrent neural network encoder and multinomial hierarchical regression decoder. In: 2017 IEEE international conference on healthcare informatics (ICHI), IEEE, pp 46\u201355. https:\/\/doi.org\/10.1109\/ICHI.2017.51, http:\/\/ieeexplore.ieee.org\/document\/8031131\/","DOI":"10.1109\/ICHI.2017.51"},{"issue":"8","key":"10195_CR33","doi-asserted-by":"publisher","first-page":"1168","DOI":"10.3390\/en10081168","volume":"10","author":"H Zheng","year":"2017","unstructured":"Zheng H, Yuan J, Chen L (2017) Short-term load forecasting using EMD-LSTM neural networks with a Xgboost algorithm for feature importance evaluation. Energies 10(8):1168. https:\/\/doi.org\/10.3390\/en10081168","journal-title":"Energies"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-020-10195-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11063-020-10195-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-020-10195-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,1,30]],"date-time":"2021-01-30T00:23:26Z","timestamp":1611966206000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11063-020-10195-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,31]]},"references-count":33,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2020,6]]}},"alternative-id":["10195"],"URL":"https:\/\/doi.org\/10.1007\/s11063-020-10195-x","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"value":"1370-4621","type":"print"},{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,31]]},"assertion":[{"value":"31 January 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}