{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T04:45:21Z","timestamp":1769575521863,"version":"3.49.0"},"reference-count":31,"publisher":"Elsevier BV","issue":"1-3","license":[{"start":{"date-parts":[[1999,10,1]],"date-time":"1999-10-01T00:00:00Z","timestamp":938736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[1999,10]]},"DOI":"10.1016\/s0925-2312(98)00125-8","type":"journal-article","created":{"date-parts":[[2002,7,25]],"date-time":"2002-07-25T12:59:42Z","timestamp":1027601982000},"page":"207-232","source":"Crossref","is-referenced-by-count":41,"title":["Dynamical recurrent neural networks towards prediction and modeling of dynamical systems"],"prefix":"10.1016","volume":"28","author":[{"given":"Alex","family":"Aussem","sequence":"first","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/S0925-2312(98)00125-8_BIB1","doi-asserted-by":"crossref","unstructured":"A. Aussem, F. Murtagh, M. Sarazin, Dynamical recurrent neural networks and pattern recognition methods for time series prediction: application to seeing and temperature forecasting in the context of ESO's VLT Astronomical Weather Station, Vistas Astron. 38 (1995) 357-374.","DOI":"10.1016\/0083-6656(94)90047-7"},{"issue":"2","key":"10.1016\/S0925-2312(98)00125-8_BIB2","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1142\/S0129065795000123","article-title":"Dynamical recurrent neural networks\u2013towards environmental time series prediction","volume":"6","author":"Aussem","year":"1995","journal-title":"Int. J. Neural Systems"},{"issue":"4","key":"10.1016\/S0925-2312(98)00125-8_BIB3","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/0925-2312(95)00054-2","article-title":"Fuzzy astronomical seeing nowcasts with a dynamical and recurrent connectionist network","volume":"12","author":"Aussem","year":"1996","journal-title":"Neurocomputing"},{"key":"10.1016\/S0925-2312(98)00125-8_BIB4","doi-asserted-by":"crossref","unstructured":"A. Aussem, F. Murtagh, Combining neural network forecasts on wavelet-transformed time series, Connection Sci. 9 (1) (1997) 113\u2013121.","DOI":"10.1080\/095400997116766"},{"key":"10.1016\/S0925-2312(98)00125-8_BIB5","unstructured":"A. Aussem, Nonlinear modeling of chaotic processes with dynamical recurrent neural networks, Proceedings of the NEURAP\u201998, Marseille, France, 1998, pp. 425\u2013433"},{"key":"10.1016\/S0925-2312(98)00125-8_BIB6","unstructured":"G.E. Box, G.M. Jenkins, Time Series Analysis: Forecasting and Control, Holden-Day, San Francisco, 1976."},{"key":"10.1016\/S0925-2312(98)00125-8_BIB7","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/0167-2789(89)90074-2","article-title":"Nonlinear prediction of chaotic time series","volume":"35","author":"Casdagli","year":"1989","journal-title":"Physica D"},{"key":"10.1016\/S0925-2312(98)00125-8_BIB8","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1109\/72.279188","article-title":"Recurrent neural networks and robust time series prediction, IEEE Trans","volume":"5","author":"Connor","year":"1994","journal-title":"Neural Networks"},{"key":"10.1016\/S0925-2312(98)00125-8_BIB9","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1207\/s15516709cog1402_1","volume":"14","author":"Elman","year":"1990","journal-title":"Finding structure in time, Cognitive Sci."},{"key":"10.1016\/S0925-2312(98)00125-8_BIB10","first-page":"120","volume":"4","author":"Frasconi","year":"1992","journal-title":"Local feedback multilayered networks, Neural Comput."},{"key":"10.1016\/S0925-2312(98)00125-8_BIB11","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1364\/JOSAB.2.000552","article-title":"Global dynamical systems, and bifurcations of vector fields","volume":"B-2","author":"Hammel","year":"1985","journal-title":"J. Opt. Soc. Amer."},{"key":"10.1016\/S0925-2312(98)00125-8_BIB12","first-page":"1735","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Long short-term memory, Neural Comput."},{"key":"10.1016\/S0925-2312(98)00125-8_BIB13","unstructured":"K. Lang, G. Hinton, Time-delay neural network architecture for speech recognition, Technical report CMU-CS-88-152, Carnegie-Mellon University, Pittsburgh, 1988."},{"key":"10.1016\/S0925-2312(98)00125-8_BIB14","doi-asserted-by":"crossref","unstructured":"R. Leighton, B. Conrad, The autoregressive backpropagation algorithm, Proceedings of the IJCNN\u201991, 1991.","DOI":"10.1109\/IJCNN.1991.155362"},{"key":"10.1016\/S0925-2312(98)00125-8_BIB15","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1080\/0020718508961129","article-title":"Input\u2013output parametric models for non-linear systems","volume":"41","author":"Leontaritis","year":"1985","journal-title":"Int. J. Control"},{"key":"10.1016\/S0925-2312(98)00125-8_BIB16","first-page":"349","volume":"7","author":"Levin","year":"1995","journal-title":"Identification using feedforward networks, Neural Comput."},{"key":"10.1016\/S0925-2312(98)00125-8_BIB17","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1175\/1520-0469(1963)020<0130:DNF>2.0.CO;2","article-title":"Deterministic nonperiodic flow","volume":"20","author":"Lorenz","year":"1963","journal-title":"J. Atmos. Sci."},{"key":"10.1016\/S0925-2312(98)00125-8_BIB18","doi-asserted-by":"crossref","unstructured":"K.S. Narendra, K. Parthasarathy, Gradient methods for the optimization of linear control systems, IEEE Trans. Neural Networks (1991) 252\u2013262.","DOI":"10.1109\/72.80336"},{"key":"10.1016\/S0925-2312(98)00125-8_BIB19","unstructured":"E. Ott, Chaos in Dynamical Systems, Cambridge University Press, Cambridge, 1993."},{"key":"10.1016\/S0925-2312(98)00125-8_BIB20","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1109\/72.279185","article-title":"Steepest descent algorithms for neural network controllers and filters","volume":"5","author":"Pich\u00e9","year":"1994","journal-title":"IEEE Trans. Neural Networks"},{"key":"10.1016\/S0925-2312(98)00125-8_BIB21","first-page":"2229","volume":"59","author":"Pineda","year":"1987","journal-title":"Generalization of back-propagation to recurrent neural networks, Phys. Rev. Lett."},{"key":"10.1016\/S0925-2312(98)00125-8_BIB22","unstructured":"M.B. Priestley, Spectral Analysis and Time Series, Academic Press, New York, 1981."},{"key":"10.1016\/S0925-2312(98)00125-8_BIB23","doi-asserted-by":"crossref","unstructured":"I. Rivals, L. Personnaz, Black-box modeling with state-space neural networks, in: I.R. Zbokowski, K.J. Hunt (Eds.), Neural Adaptive Control Technology, World Scientific, Singapore 1995.","DOI":"10.1142\/9789812830388_0008"},{"key":"10.1016\/S0925-2312(98)00125-8_BIB24","doi-asserted-by":"crossref","unstructured":"D.E. Rumelhart, G.E. Hinton, R.J. Williams, Learning internal representations by error propagation, in: D.E. Rumelhart, J.L. McClelland (Eds.), Parallel Distributed Processing Explorations in the Microstructure of Cognition, vol. 1, MIT Press, Cambridge, MA, Bradfords Books, 1986, pp. 318\u2013362.","DOI":"10.21236\/ADA164453"},{"key":"10.1016\/S0925-2312(98)00125-8_BIB25","first-page":"243","volume":"4","author":"Schmidhuber","year":"1992","journal-title":"A fixed size storage O(n3) time complexity learning algorithm for fully recurrent continually running networks, Neural Comput."},{"key":"10.1016\/S0925-2312(98)00125-8_BIB26","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1109\/72.279186","article-title":"Back propagation through adjoints for the identification of nonlinear dynamic systems using recurrent neural networks","volume":"5","author":"Srinivasan","year":"1994","journal-title":"IEEE Trans. Neural Networks"},{"key":"10.1016\/S0925-2312(98)00125-8_BIB27","doi-asserted-by":"crossref","unstructured":"H. Tong, Non Linear Time Series, Clarendon Press, Oxford, 1990.","DOI":"10.1093\/oso\/9780198522249.001.0001"},{"key":"10.1016\/S0925-2312(98)00125-8_BIB28","unstructured":"E.A. Wan, Finite impulse response neural networks with applications in time series prediction, Ph.D. Thesis, Stanford University, 1993."},{"key":"10.1016\/S0925-2312(98)00125-8_BIB29","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1142\/S0129065790000102","article-title":"Predicting the future: a connectionist approach","volume":"1","author":"Weigend","year":"1990","journal-title":"Int. J. Neural Systems"},{"key":"10.1016\/S0925-2312(98)00125-8_BIB30","unstructured":"P. Werbos, Beyond regression: new tools for prediction and analysis in behavioral sciences, Ph.D. Thesis, Harvard University, 1974."},{"key":"10.1016\/S0925-2312(98)00125-8_BIB31","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1080\/09540098908915631","article-title":"Experimental analysis of the real-time recurrent learning algorithm","volume":"1","author":"Williams","year":"1989","journal-title":"Connection Sci."}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231298001258?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231298001258?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2023,12,31]],"date-time":"2023-12-31T05:38:37Z","timestamp":1704001117000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231298001258"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[1999,10]]},"references-count":31,"journal-issue":{"issue":"1-3","published-print":{"date-parts":[[1999,10]]}},"alternative-id":["S0925231298001258"],"URL":"https:\/\/doi.org\/10.1016\/s0925-2312(98)00125-8","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[1999,10]]}}}