{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T21:17:26Z","timestamp":1743110246375,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":32,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819980819"},{"type":"electronic","value":"9789819980826"}],"license":[{"start":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T00:00:00Z","timestamp":1700006400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T00:00:00Z","timestamp":1700006400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-99-8082-6_42","type":"book-chapter","created":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T16:08:09Z","timestamp":1699978089000},"page":"550-560","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Small-World Echo State Networks for\u00a0Nonlinear Time-Series Prediction"],"prefix":"10.1007","author":[{"given":"Shu","family":"Mo","sequence":"first","affiliation":[]},{"given":"Kai","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Weibing","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yongping","family":"Pan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,15]]},"reference":[{"key":"42_CR1","series-title":"Natural Computing Series","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-1687-6","volume-title":"Reservoir Computing","year":"2021","unstructured":"Nakajima, K., Fischer, I. (eds.): Reservoir Computing. NCS, Springer, Singapore (2021). https:\/\/doi.org\/10.1007\/978-981-13-1687-6"},{"key":"42_CR2","unstructured":"Jaeger, H.: The \u201cecho state\u201d approach to analysing and training recurrent neural networks-with an erratum note. In: German National Research Center for Information Technology GMD Technical Report, Bonn, Germany, vol. 148, no. 34, p. 13 (2001)"},{"issue":"1","key":"42_CR3","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1109\/TASE.2016.2582213","volume":"14","author":"J Park","year":"2016","unstructured":"Park, J., Lee, B., Kang, S., Kim, P.Y., Kim, H.J.: Online learning control of hydraulic excavators based on echo-state networks. IEEE Trans. Autom. Sci. Eng. 14(1), 249\u2013259 (2016)","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"42_CR4","doi-asserted-by":"crossref","unstructured":"Schwedersky, B.B., Flesch, R.C.C., Dangui, H.A.S., Iervolino, L.A.: Practical nonlinear model predictive control using an echo state network model. In: International Joint Conference on Neural Networks, Rio de Janeiro, Brazil, 08\u201313 July 2018, pp. 1\u20138. IEEE (2018)","DOI":"10.1109\/IJCNN.2018.8489446"},{"issue":"Oct.","key":"42_CR5","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.engappai.2019.06.011","volume":"85","author":"JP Jordanou","year":"2019","unstructured":"Jordanou, J.P., Antonelo, E.A., Camponogara, E.: Online learning control with echo state networks of an oil production platform. Eng. Appl. Artif. Intell. 85(Oct.), 214\u2013228 (2019)","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"7","key":"42_CR6","doi-asserted-by":"publisher","first-page":"3009","DOI":"10.1109\/TCYB.2019.2931877","volume":"50","author":"Q Chen","year":"2019","unstructured":"Chen, Q., Shi, H., Sun, M.: Echo state network-based backstepping adaptive iterative learning control for strict-feedback systems: an error-tracking approach. IEEE Trans. Cybern. 50(7), 3009\u20133022 (2019)","journal-title":"IEEE Trans. Cybern."},{"key":"42_CR7","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1007\/978-3-030-89092-6_53","volume-title":"Intelligent Robotics and Applications","author":"R Wu","year":"2021","unstructured":"Wu, R., Li, Z., Pan, Y.: Adaptive echo state network robot control with guaranteed parameter convergence. In: Liu, X.-J., Nie, Z., Yu, J., Xie, F., Song, R. (eds.) ICIRA 2021. LNCS (LNAI), vol. 13016, pp. 587\u2013595. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-89092-6_53"},{"key":"42_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1007\/978-3-030-92270-2_23","volume-title":"Neural Information Processing","author":"R Wu","year":"2021","unstructured":"Wu, R., Nakajima, K., Pan, Y.: Performance improvement of\u00a0FORCE learning for\u00a0chaotic echo state networks. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds.) ICONIP 2021. LNCS, vol. 13109, pp. 262\u2013272. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-92270-2_23"},{"issue":"4","key":"42_CR9","doi-asserted-by":"publisher","first-page":"11244","DOI":"10.1109\/LRA.2022.3199034","volume":"7","author":"K Tanaka","year":"2022","unstructured":"Tanaka, K., Minami, Y., Tokudome, Y., Inoue, K., Kuniyoshi, Y., Nakajima, K.: Continuum-body-pose estimation from partial sensor information using recurrent neural networks. IEEE Robot. Autom. Lett. 7(4), 11244\u201311251 (2022)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"42_CR10","doi-asserted-by":"crossref","unstructured":"Li, Y., Hu, K., Nakajima, K., Pan, Y.: Composite FORCE learning of chaotic echo state networks for time-series prediction. In: Chinese Control Conference, Heifei, China, 25\u201327 July 2022, pp. 7355\u20137360 (2022)","DOI":"10.23919\/CCC55666.2022.9901897"},{"issue":"5","key":"42_CR11","doi-asserted-by":"publisher","first-page":"1364","DOI":"10.1109\/TNN.2007.894082","volume":"18","author":"Z Deng","year":"2007","unstructured":"Deng, Z., Zhang, Y.: Collective behavior of a small-world recurrent neural system with scale-free distribution. IEEE Trans. Neural Netw. 18(5), 1364\u20131375 (2007)","journal-title":"IEEE Trans. Neural Netw."},{"issue":"3","key":"42_CR12","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1016\/j.neunet.2007.04.014","volume":"20","author":"Y Xue","year":"2007","unstructured":"Xue, Y., Yang, L., Haykin, S.: Decoupled echo state networks with lateral inhibition. Neural Netw. 20(3), 365\u2013376 (2007)","journal-title":"Neural Netw."},{"issue":"1","key":"42_CR13","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1109\/TNN.2010.2089641","volume":"22","author":"A Rodan","year":"2010","unstructured":"Rodan, A., Tino, P.: Minimum complexity echo state network. IEEE Trans. Neural Netw. 22(1), 131\u2013144 (2010)","journal-title":"IEEE Trans. Neural Netw."},{"issue":"3","key":"42_CR14","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1063\/1.4746765","volume":"22","author":"H Cui","year":"2012","unstructured":"Cui, H., Liu, X., Li, L.: The architecture of dynamic reservoir in the echo state network. Chaos 22(3), 455 (2012)","journal-title":"Chaos"},{"issue":"2","key":"42_CR15","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1109\/TNNLS.2016.2514275","volume":"28","author":"J Qiao","year":"2016","unstructured":"Qiao, J., Li, F., Han, H., Li, W.: Growing echo-state network with multiple subreservoirs. IEEE Trans. Neural Netw. Learn. Syst. 28(2), 391\u2013404 (2016)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"42_CR16","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.neunet.2019.01.002","volume":"112","author":"Y Kawai","year":"2019","unstructured":"Kawai, Y., Park, J., Asada, M.: A small-world topology enhances the echo state property and signal propagation in reservoir computing. Neural Netw. 112, 15\u201323 (2019)","journal-title":"Neural Netw."},{"issue":"9","key":"42_CR17","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1038\/s42256-021-00376-1","volume":"3","author":"LE Su\u00e1rez","year":"2021","unstructured":"Su\u00e1rez, L.E., Richards, B.A., Lajoie, G., Misic, B.: Learning function from structure in neuromorphic networks. Nat. Mach. Intell. 3(9), 771\u2013786 (2021)","journal-title":"Nat. Mach. Intell."},{"key":"42_CR18","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.ins.2015.11.017","volume":"364\u2013365","author":"MH Yusoff","year":"2016","unstructured":"Yusoff, M.H., Chrol-Cannon, J., Jin, Y.: Modeling neural plasticity in echo state networks for classification and regression. Inf. Sci. 364\u2013365, 184\u2013196 (2016)","journal-title":"Inf. Sci."},{"key":"42_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/978-3-540-74690-4_3","volume-title":"Artificial Neural Networks \u2013 ICANN 2007","author":"\u0160 Babinec","year":"2007","unstructured":"Babinec, \u0160, Posp\u00edchal, J.: Improving the prediction accuracy of echo state neural networks by anti-oja\u2019s learning. In: de S\u00e1, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds.) ICANN 2007. LNCS, vol. 4668, pp. 19\u201328. Springer, Heidelberg (2007). https:\/\/doi.org\/10.1007\/978-3-540-74690-4_3"},{"issue":"2","key":"42_CR20","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1088\/0954-898X_10_2_001","volume":"10","author":"G Castellani","year":"1999","unstructured":"Castellani, G., Intrator, N., Shouval, H., Cooper, L.: Solutions of the BCM learning rule in a network of lateral interacting nonlinear neurons. Netw. Comput. Neural Syst. 10(2), 111 (1999)","journal-title":"Netw. Comput. Neural Syst."},{"issue":"10","key":"42_CR21","doi-asserted-by":"publisher","first-page":"11254","DOI":"10.1109\/TCYB.2021.3060466","volume":"52","author":"X Wang","year":"2021","unstructured":"Wang, X., Jin, Y., Hao, K.: Computational modeling of structural synaptic plasticity in echo state networks. IEEE Trans. Cybern. 52(10), 11254\u201311266 (2021)","journal-title":"IEEE Trans. Cybern."},{"issue":"1","key":"42_CR22","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1004031","volume":"11","author":"M Fauth","year":"2015","unstructured":"Fauth, M., W\u00f6rg\u00f6tter, F., Tetzlaff, C.: The formation of multi-synaptic connections by the interaction of synaptic and structural plasticity and their functional consequences. PLoS Comput. Biol. 11(1), e1004031 (2015)","journal-title":"PLoS Comput. Biol."},{"key":"42_CR23","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/j.ins.2021.03.013","volume":"566","author":"L Patan\u00e8","year":"2021","unstructured":"Patan\u00e8, L., Xibilia, M.G.: Echo-state networks for soft sensor design in an SRU process. Inf. Sci. 566, 195\u2013214 (2021)","journal-title":"Inf. Sci."},{"key":"42_CR24","doi-asserted-by":"publisher","first-page":"705","DOI":"10.1016\/j.neucom.2020.05.127","volume":"461","author":"GB Morales","year":"2021","unstructured":"Morales, G.B., Mirasso, C.R., Soriano, M.C.: Unveiling the role of plasticity rules in reservoir computing. Neurocomputing 461, 705\u2013715 (2021)","journal-title":"Neurocomputing"},{"key":"42_CR25","doi-asserted-by":"publisher","unstructured":"Wang, X., Jin, Y., Du, W., Wang, J.: Evolving dual-threshold Bienenstock-Cooper-Munro learning rules in echo state networks. IEEE Trans. Neural Netw. Learn. Syst., 1\u201312 (2022). https:\/\/doi.org\/10.1109\/TNNLS.2022.3184004","DOI":"10.1109\/TNNLS.2022.3184004"},{"issue":"6684","key":"42_CR26","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1038\/30918","volume":"393","author":"DJ Watts","year":"1998","unstructured":"Watts, D.J., Strogatz, S.H.: Collective dynamics of \u2018small-world\u2019 networks. Nature 393(6684), 440\u2013442 (1998)","journal-title":"Nature"},{"issue":"4\u20136","key":"42_CR27","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/S0375-9601(99)00757-4","volume":"263","author":"ME Newman","year":"1999","unstructured":"Newman, M.E., Watts, D.J.: Renormalization group analysis of the small-world network model. Phys. Lett. A 263(4\u20136), 341\u2013346 (1999)","journal-title":"Phys. Lett. A"},{"issue":"5","key":"42_CR28","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1016\/j.tins.2010.02.001","volume":"33","author":"E Benito","year":"2010","unstructured":"Benito, E., Barco, A.: Creb\u2019s control of intrinsic and synaptic plasticity: implications for creb-dependent memory models. Trends Neuralsci. 33(5), 230\u2013240 (2010)","journal-title":"Trends Neuralsci."},{"issue":"4","key":"42_CR29","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1016\/j.neuron.2009.07.018","volume":"63","author":"D Sussillo","year":"2009","unstructured":"Sussillo, D., Abbott, L.F.: Generating coherent patterns of activity from chaotic neural networks. Neuron 63(4), 544\u2013557 (2009)","journal-title":"Neuron"},{"issue":"2","key":"42_CR30","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0191527","volume":"13","author":"B DePasquale","year":"2018","unstructured":"DePasquale, B., Cueva, C.J., Rajan, K., Escola, G.S., Abbott, L.: full-FORCE: a target-based method for training recurrent networks. PLoS ONE 13(2), e0191527 (2018)","journal-title":"PLoS ONE"},{"issue":"9","key":"42_CR31","doi-asserted-by":"publisher","first-page":"2603","DOI":"10.1109\/TAC.2015.2495232","volume":"61","author":"Y Pan","year":"2016","unstructured":"Pan, Y., Yu, H.: Composite learning from adaptive dynamic surface control. IEEE Trans. Autom. Control. 61(9), 2603\u20132609 (2016)","journal-title":"IEEE Trans. Autom. Control."},{"issue":"5667","key":"42_CR32","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1126\/science.1091277","volume":"304","author":"H Jaeger","year":"2004","unstructured":"Jaeger, H., Haas, H.: Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304(5667), 78\u201380 (2004)","journal-title":"Science"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8082-6_42","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T00:19:17Z","timestamp":1730506757000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8082-6_42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,15]]},"ISBN":["9789819980819","9789819980826"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8082-6_42","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,11,15]]},"assertion":[{"value":"15 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"650","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"51% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.14","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.46","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}