{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,11,12]],"date-time":"2022-11-12T12:25:56Z","timestamp":1668255956462},"reference-count":11,"publisher":"World Scientific Pub Co Pte Lt","issue":"06","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[2008,12]]},"abstract":"<jats:p> A method based on neural network with Back-Propagation Algorithm (BPA) and Adaptive Smoothing Errors (ASE), and a Genetic Algorithm (GA) employing a new concept named Adaptive Relaxation (GAAR) is presented in this paper to construct learning system that can find an Adaptive Mesh points (AM) in fluid problems. AM based on reallocation scheme is implemented on different types of two steps channels by using a three layer neural network with GA. Results of numerical experiments using Finite Element Method (FEM) are discussed. Such discussion is intended to validate the process and to demonstrate the performance of the proposed learning system on three types of two steps channels. It appears that training is fast enough and accurate due to the optimal values of weights by using a few numbers of patterns. Results confirm that the presented neural network with the proposed GA consistently finds better solutions than the conventional neural network. <\/jats:p>","DOI":"10.1142\/s021821300800431x","type":"journal-article","created":{"date-parts":[[2009,1,7]],"date-time":"2009-01-07T10:43:05Z","timestamp":1231324985000},"page":"1089-1108","source":"Crossref","is-referenced-by-count":5,"title":["COMPUTING AN ADAPTIVE MESH IN FLUID PROBLEMS USING NEURAL NETWORK AND GENETIC ALGORITHM WITH ADAPTIVE RELAXATION"],"prefix":"10.1142","volume":"17","author":[{"given":"NAMEER N. EL.","family":"EMAM","sequence":"first","affiliation":[{"name":"Department of Computer Science, Philadelphia University, Amman, Jordan"}]},{"given":"RASHEED ABDUL","family":"SHAHEED","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Philadelphia University, Amman, Jordan"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"reference":[{"key":"rf1","first-page":"175","volume":"9","author":"EL-Emam Nameer N.","journal-title":"Information Journal"},{"key":"rf2","first-page":"195","volume":"32","author":"Carey G. F.","journal-title":"Applied Numerical Math"},{"key":"rf3","doi-asserted-by":"publisher","DOI":"10.1002\/fld.320"},{"key":"rf4","first-page":"779","volume":"14","author":"Zgonc Kornelija","journal-title":"IEEE Trans. on UFFC"},{"key":"rf5","first-page":"115","volume":"2","author":"Zhihua Z.","journal-title":"Knowledge and Information System"},{"key":"rf6","volume-title":"Neural Networks \u2013 A Comprehensive Foundations","author":"Haykin S.","year":"1999"},{"key":"rf8","doi-asserted-by":"publisher","DOI":"10.1162\/106365602760972767"},{"key":"rf9","volume-title":"Multi-Objective Optimization Using Evolutionary Algorithms","author":"Deb K.","year":"2001"},{"key":"rf10","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/19.1.37"},{"key":"rf11","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2004.03.011"},{"key":"rf14","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-006-0041-2"}],"container-title":["International Journal on Artificial Intelligence Tools"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S021821300800431X","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T02:27:23Z","timestamp":1565144843000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S021821300800431X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2008,12]]},"references-count":11,"journal-issue":{"issue":"06","published-online":{"date-parts":[[2011,11,21]]},"published-print":{"date-parts":[[2008,12]]}},"alternative-id":["10.1142\/S021821300800431X"],"URL":"https:\/\/doi.org\/10.1142\/s021821300800431x","relation":{},"ISSN":["0218-2130","1793-6349"],"issn-type":[{"value":"0218-2130","type":"print"},{"value":"1793-6349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2008,12]]}}}