{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T20:58:26Z","timestamp":1743109106695,"version":"3.40.3"},"publisher-location":"Berlin, Heidelberg","reference-count":16,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"type":"print","value":"9783540287551"},{"type":"electronic","value":"9783540287568"}],"license":[{"start":{"date-parts":[[2005,1,1]],"date-time":"2005-01-01T00:00:00Z","timestamp":1104537600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2005,1,1]],"date-time":"2005-01-01T00:00:00Z","timestamp":1104537600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2005]]},"DOI":"10.1007\/11550907_29","type":"book-chapter","created":{"date-parts":[[2021,2,12]],"date-time":"2021-02-12T16:24:41Z","timestamp":1613147081000},"page":"175-180","source":"Crossref","is-referenced-by-count":4,"title":["Time Delay Learning by Gradient Descent in Recurrent Neural Networks"],"prefix":"10.1007","author":[{"given":"Romuald","family":"Bon\u00e9","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hubert","family":"Cardot","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"unstructured":"Jin, L., Nikiforuk, N., Gupta, M.M.: Uniform Approximation of Nonlinear Dynamic Systems Using Dynamic Neural Networks. In: International Conference on Artificial Neural Networks, pp. 191\u2013196 (1995)","key":"29_CR1"},{"key":"29_CR2","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1109\/72.279181","volume":"5","author":"Y. Bengio","year":"1994","unstructured":"Bengio, Y., Simard, P., Frasconi, P.: Learning Long-Term Dependencies with Gradient Descent is Difficult. IEEE Transactions on Neural Networks\u00a05, 157\u2013166 (1994)","journal-title":"IEEE Transactions on Neural Networks"},{"key":"29_CR3","first-page":"13","volume":"7","author":"T. Lin","year":"1996","unstructured":"Lin, T., Horne, B.G., Tino, P., Giles, C.L.: Learning Long-Term Dependencies in NARX Recurrent Neural Networks. IEEE Transactions on Neural Networks\u00a07, 13\u201329 (1996)","journal-title":"IEEE Transactions on Neural Networks"},{"key":"29_CR4","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1109\/72.750549","volume":"10","author":"P. Campolucci","year":"1999","unstructured":"Campolucci, P., Uncini, A., Piazza, F., Rao, B.D.: On-Line Learning Algorithms for Locally Recurrent Neural Networks. IEEE Transactions on Neural Networks\u00a010, 253\u2013271 (1999)","journal-title":"IEEE Transactions on Neural Networks"},{"key":"29_CR5","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1109\/72.279187","volume":"5","author":"A.C. Tsoi","year":"1994","unstructured":"Tsoi, A.C., Back, A.D.: Locally Recurrent Globally Feedforward Networks: A Critical Review of Architectures. IEEE Transactions on Neural Networks\u00a05, 229\u2013239 (1994)","journal-title":"IEEE Transactions on Neural Networks"},{"doi-asserted-by":"crossref","unstructured":"Guignot, J., Gallinari, P.: Recurrent Neural Networks with Delays. In: International Conference on Artificial Neural Networks, pp. 389\u2013392 (1994)","key":"29_CR6","DOI":"10.1007\/978-1-4471-2097-1_90"},{"key":"29_CR7","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/S0925-2312(01)00654-3","volume":"48","author":"R. Bon\u00e9","year":"2002","unstructured":"Bon\u00e9, R., Crucianu, M., Asselin de Beauville, J.-P.: Learning Long-Term Dependencies by the Selective Addition of Time-Delayed Connections to Recurrent Neural Networks. NeuroComputing\u00a048, 251\u2013266 (2002)","journal-title":"NeuroComputing"},{"key":"29_CR8","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1109\/72.774220","volume":"10","author":"R.J. Duro","year":"1999","unstructured":"Duro, R.J., Santos Reyes, J.: Discrete-Time Backpropagation for Training Synaptic Delay-Based Artificial Neural Networks. IEEE Transactions on Neural Networks\u00a010, 779\u2013789 (1999)","journal-title":"IEEE Transactions on Neural Networks"},{"unstructured":"Pearlmutter, B.A.: Dynamic Recurrent Neural Networks. Research Report CMU-CS-90-196, Carnegie Mellon University School of Computer Science (1990)","key":"29_CR9"},{"key":"29_CR10","doi-asserted-by":"crossref","first-page":"318","DOI":"10.7551\/mitpress\/5236.001.0001","volume-title":"Parallel Distributed Processing: Explorations in the Microstructure of Cognition","author":"D.E. Rumelhart","year":"1986","unstructured":"Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning Internal Representations by Error Propagation. In: Rumelhart, D.E., McClelland, J. (eds.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition, pp. 318\u2013362. MIT Press, Cambridge (1986)"},{"key":"29_CR11","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1142\/S0129065790000102","volume":"1","author":"A.S. Weigend","year":"1990","unstructured":"Weigend, A.S., Huberman, B.A., Rumelhart, D.E.: Predicting the Future: A Connectionist Approach. International Journal of Neural Systems\u00a01, 193\u2013209 (1990)","journal-title":"International Journal of Neural Systems"},{"doi-asserted-by":"crossref","unstructured":"Mackey, M., Glass, L.: Oscillations and chaos in physiological control systems. Science, 197\u2013287 (1977)","key":"29_CR12","DOI":"10.1126\/science.267326"},{"doi-asserted-by":"crossref","unstructured":"Bon\u00e9, R., Crucianu, M., Verley, G., Asselin de Beauville, J.-P.: A Bounded Exploration Approach to Constructive Algorithms for Recurrent Neural Networks. In: International Joint Conference on Neural Networks, 6 p (2000)","key":"29_CR13","DOI":"10.1109\/IJCNN.2000.861276"},{"unstructured":"Back, A., Wan, E.A., Lawrence, S., Tsoi, A.C.: A Unifying View of some Training Algorithms for Multilayer Perceptrons with FIR Filter Synapses. Neural Networks for Signal Processing IV, 146\u2013154 (1994)","key":"29_CR14"},{"unstructured":"Aussem, A.: Nonlinear Modeling of Chaotic Processes with Dynamical Recurrent Neural Networks. Neural Networks and Their Applications, 425\u2013433 (1998)","key":"29_CR15"},{"doi-asserted-by":"crossref","unstructured":"Gers, F., Eck, D., Schmidhuber, J.: Applying LSTM to Time Series Predictable Through Time-Window Approaches. In: Int. Conference on Artificial Neural Networks, pp. 669\u2013675 (2001)","key":"29_CR16","DOI":"10.1007\/3-540-44668-0_93"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks: Formal Models and Their Applications \u2013 ICANN 2005"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/11550907_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,12]],"date-time":"2021-02-12T16:35:20Z","timestamp":1613147720000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/11550907_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2005]]},"ISBN":["9783540287551","9783540287568"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/11550907_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2005]]}}}