{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T17:16:40Z","timestamp":1648660600496},"reference-count":6,"publisher":"World Scientific Pub Co Pte Lt","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Comp. Intel. Appl."],"published-print":{"date-parts":[[2001,12]]},"abstract":"<jats:p> Time-series predictions by artificial neural networks (ANNs) are traditionally formulated as unconstrained optimization problems. As an unconstrained formulation provides little guidance on search directions when a search gets stuck in a poor local minimum, we have proposed to use a constrained formulation in order to use constraint violations to provide additional guidance. In this paper, we formulate ANN learning with cross-validations for time-series predictions as a non-differentiable nonlinear constrained optimization problem. Based on our theory of Lagrange multipliers for discrete constrained optimization, we propose an efficient learning algorithm, called violation guided back-propagation (VGBP), that computes an approximate gradient using back-propagation (BP), that introduces annealing to avoid blind acceptance of trial points, and that applies a relax-and-tighten (R&amp;T) strategy to achieve faster convergence. Extensive experimental results on well-known benchmarks, when compared to previous work, show one to two orders-of-magnitude improvement in prediction quality, while using less weights. <\/jats:p>","DOI":"10.1142\/s1469026801000317","type":"journal-article","created":{"date-parts":[[2002,7,27]],"date-time":"2002-07-27T10:58:59Z","timestamp":1027767539000},"page":"383-397","source":"Crossref","is-referenced-by-count":2,"title":["VIOLATION-GUIDED NEURAL-NETWORK LEARNING FOR CONSTRAINED FORMULATIONS IN TIME-SERIES PREDICTIONS"],"prefix":"10.1142","volume":"01","author":[{"given":"BENJAMIN W.","family":"WAH","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, and the Coordinated Science Laboratory, University of Illinois, Urbana-Champaign, Urbana, IL 61801, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"MINGLUN","family":"QIAN","sequence":"additional","affiliation":[{"name":"Department of Computer Science, and the Coordinated Science Laboratory, University of Illinois, Urbana-Champaign, Urbana, IL 61801, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2012,1,25]]},"reference":[{"key":"p_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0925-2312(98)00125-8"},{"key":"p_3","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065795000123"},{"key":"p_4","doi-asserted-by":"publisher","DOI":"10.1080\/00207179008934126"},{"key":"p_5","doi-asserted-by":"publisher","DOI":"10.1207\/s15516709cog1402_1"},{"key":"p_6","doi-asserted-by":"publisher","DOI":"10.1109\/72.728396"},{"key":"p_11","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1992.4.4.473"}],"container-title":["International Journal of Computational Intelligence and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S1469026801000317","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T13:50:26Z","timestamp":1565185826000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S1469026801000317"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2001,12]]},"references-count":6,"journal-issue":{"issue":"04","published-online":{"date-parts":[[2012,1,25]]},"published-print":{"date-parts":[[2001,12]]}},"alternative-id":["10.1142\/S1469026801000317"],"URL":"https:\/\/doi.org\/10.1142\/s1469026801000317","relation":{},"ISSN":["1469-0268","1757-5885"],"issn-type":[{"value":"1469-0268","type":"print"},{"value":"1757-5885","type":"electronic"}],"subject":[],"published":{"date-parts":[[2001,12]]}}}