{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,10,24]],"date-time":"2023-10-24T05:12:22Z","timestamp":1698124342227},"reference-count":10,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2006,12,13]],"date-time":"2006-12-13T00:00:00Z","timestamp":1165968000000},"content-version":"vor","delay-in-days":5460,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Circuit Theory &amp; Apps"],"published-print":{"date-parts":[[1992,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This paper presents a simplified approach to neural optimization in the presence of linear equality constraints. In contrast to the standard Lagrangian approach, the constraints simplify the final neural circuit instead of complicating it. the number of elements used is also significantly reduced. Instead of <jats:italic>n<\/jats:italic> + <jats:italic>t<\/jats:italic> integrators we need only <jats:italic>n<\/jats:italic> \u2013 <jats:italic>t<\/jats:italic>. There is also a similar saving in the number of preprocessing non\u2010linear devices. Elimination of the constraints allows a large speed\u2010up of the solution.<\/jats:p>","DOI":"10.1002\/cta.4490200108","type":"journal-article","created":{"date-parts":[[2007,7,2]],"date-time":"2007-07-02T02:16:23Z","timestamp":1183342583000},"page":"93-98","source":"Crossref","is-referenced-by-count":3,"title":["Neural network for non\u2010linear programming with linear equality constraints"],"prefix":"10.1002","volume":"20","author":[{"given":"Stanis\u0142saw","family":"Osowski","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2006,12,13]]},"reference":[{"key":"e_1_2_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/31.1783"},{"key":"e_1_2_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCS.1986.1085953"},{"key":"e_1_2_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/31.52732"},{"key":"e_1_2_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISCAS.1990.112594"},{"key":"e_1_2_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCS.1984.1085482"},{"key":"e_1_2_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/31.31318"},{"key":"e_1_2_1_8_2","unstructured":"S.Osowski Neural networks in minimax programming' Proc. Int. Workshop on Cellular Neural Networks and Applications Budapest 1990 pp.261\u2013269."},{"key":"e_1_2_1_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/5.58323"},{"key":"e_1_2_1_10_2","volume-title":"Practical Optimization","author":"Gill P.","year":"1981"},{"key":"e_1_2_1_11_2","volume-title":"Test Examples For Nonlinear Programming Codes","author":"Schittkowski K.","year":"1981"}],"container-title":["International Journal of Circuit Theory and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1002%2Fcta.4490200108","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/cta.4490200108","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T15:33:12Z","timestamp":1698075192000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/cta.4490200108"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[1992,1]]},"references-count":10,"journal-issue":{"issue":"1","published-print":{"date-parts":[[1992,1]]}},"alternative-id":["10.1002\/cta.4490200108"],"URL":"https:\/\/doi.org\/10.1002\/cta.4490200108","archive":["Portico"],"relation":{},"ISSN":["0098-9886","1097-007X"],"issn-type":[{"value":"0098-9886","type":"print"},{"value":"1097-007X","type":"electronic"}],"subject":[],"published":{"date-parts":[[1992,1]]}}}