{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T12:34:19Z","timestamp":1742992459586,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":10,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811575297"},{"type":"electronic","value":"9789811575303"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-981-15-7530-3_21","type":"book-chapter","created":{"date-parts":[[2020,8,13]],"date-time":"2020-08-13T15:50:27Z","timestamp":1597333827000},"page":"286-297","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Optimizing the Reservoir Connection Structure Using Binary Symbiotic Organisms Search Algorithm: A Case Study on Electric Load Forecasting"],"prefix":"10.1007","author":[{"given":"Lina","family":"Pan","sequence":"first","affiliation":[]},{"given":"Bo","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,8,14]]},"reference":[{"key":"21_CR1","unstructured":"Research and application of neural network and its combination model in time series forecasting. Ph.D. thesis, LanZhou University (2018)"},{"key":"21_CR2","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.compstruc.2014.03.007","volume":"139","author":"MY Cheng","year":"2014","unstructured":"Cheng, M.Y., Prayogo, D.: Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput. Struct. 139, 98\u2013112 (2014)","journal-title":"Comput. Struct."},{"key":"21_CR3","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1016\/j.energy.2013.06.007","volume":"57","author":"A Deihimi","year":"2013","unstructured":"Deihimi, A., Orang, O., Showkati, H.: Short-term electric load and temperature forecasting using wavelet echo state networks with neural reconstruction. Energy 57, 382\u2013401 (2013)","journal-title":"Energy"},{"issue":"7","key":"21_CR4","doi-asserted-by":"publisher","first-page":"1534","DOI":"10.1016\/j.neucom.2008.12.020","volume":"72","author":"X Dutoit","year":"2009","unstructured":"Dutoit, X., Schrauwen, B., Van Campenhout, J., Stroobandt, D., Van Brussel, H., Nuttin, M.: Pruning and regularization in reservoir computing. Neurocomputing 72(7), 1534\u20131546 (2009)","journal-title":"Neurocomputing"},{"key":"21_CR5","first-page":"34","volume":"148","author":"H Jaeger","year":"2001","unstructured":"Jaeger, H.: The \u201cecho state\u201d approach to analysing and training recurrent neural networks-with an erratum note. Bonn, Germany: German Natl. Res. Center Inf. Technol. GMD Tech. Rep. 148, 34 (2001)","journal-title":"Bonn, Germany: German Natl. Res. Center Inf. Technol. GMD Tech. Rep."},{"key":"21_CR6","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.atmosres.2013.11.013","volume":"144","author":"P Nastos","year":"2014","unstructured":"Nastos, P., Paliatsos, A., Koukouletsos, K., Larissi, I., Moustris, K.: Artificial neural networks modeling for forecasting the maximum daily total precipitation at Athens, Greece. Atmosph. Res. 144, 141\u2013150 (2014)","journal-title":"Atmosph. Res."},{"issue":"1","key":"21_CR7","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1162\/neco.2007.19.1.111","volume":"19","author":"MC Ozturk","year":"2007","unstructured":"Ozturk, M.C., Xu, D., Pr\u00edncipe, J.C.: Analysis and design of echo state networks. Neural Comput. 19(1), 111\u2013138 (2007)","journal-title":"Neural Comput."},{"issue":"10","key":"21_CR8","doi-asserted-by":"publisher","first-page":"2177","DOI":"10.1016\/j.neucom.2010.01.015","volume":"73","author":"Q Song","year":"2010","unstructured":"Song, Q., Feng, Z.: Effects of connectivity structure of complex echo state network on its prediction performance for nonlinear time series. Neurocomputing 73(10), 2177\u20132185 (2010)","journal-title":"Neurocomputing"},{"key":"21_CR9","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.knosys.2015.06.003","volume":"86","author":"H Wang","year":"2015","unstructured":"Wang, H., Yan, X.: Optimizing the echo state network with a binary particle swarm optimization algorithm. Knowl.-Based Syst. 86, 182\u2013193 (2015)","journal-title":"Knowl.-Based Syst."},{"issue":"2","key":"21_CR10","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/j.epsr.2017.01.035","volume":"146","author":"X Zhang","year":"2017","unstructured":"Zhang, X., Wang, J., Zhang, K.: Short-term electric load forecasting based on singular spectrum analysis and support vector machine optimized by cuckoo search algorithm. Electric Power Syst. Res. 146(2), 270\u2013285 (2017)","journal-title":"Electric Power Syst. Res."}],"container-title":["Communications in Computer and Information Science","Big Data and Security"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-15-7530-3_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,8,13]],"date-time":"2020-08-13T15:55:48Z","timestamp":1597334148000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-981-15-7530-3_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9789811575297","9789811575303"],"references-count":10,"URL":"https:\/\/doi.org\/10.1007\/978-981-15-7530-3_21","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"14 August 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICBDS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Big Data and Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nanjing","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":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 December 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 December 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icbds2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2019.icbds.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":"OJS\/PKP","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"251","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":"37","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":"12","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":"15% - 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":"3","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":"3","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)"}}]}}