1996
DOI: 10.1016/0045-7949(95)00321-5
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Application of artificial neural networks for the optimum design of a laminated plate

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Cited by 10 publications
(7 citation statements)
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“…One of the important functions of the brain that ANN imitates is the learning process which enables the ANN to use example problems and their solutions to learn the basic solution procedure and apply it to solve similar problems. As a result of ANNs effective capabilities in solving complex problems, it is being used in several branches of science and engineering and composite optimization is another area of application which has been explored in recent studies, see Berke et al (1993), Jayatheertha et al (1996), Kodiyalam & Gurumoorthy (1996) and Chen et al (1999). An overview of the subject as applied to structural optimization was given by Hajela & Berke (1992).…”
Section: Artificial Neural Network (Ann)mentioning
confidence: 99%
“…One of the important functions of the brain that ANN imitates is the learning process which enables the ANN to use example problems and their solutions to learn the basic solution procedure and apply it to solve similar problems. As a result of ANNs effective capabilities in solving complex problems, it is being used in several branches of science and engineering and composite optimization is another area of application which has been explored in recent studies, see Berke et al (1993), Jayatheertha et al (1996), Kodiyalam & Gurumoorthy (1996) and Chen et al (1999). An overview of the subject as applied to structural optimization was given by Hajela & Berke (1992).…”
Section: Artificial Neural Network (Ann)mentioning
confidence: 99%
“…902 The macro material properties of composite material can be obtained from constituent material properties using micromechanics. 76,91,106,113,274,291,307,311,341,400,424,425,545,618,654,696,886,917,927,953,982,988 The optimization algorithm may be coupled with a response surface, 71,292,382,396,414,423,495,559,602,604,605,616,624,625,635,637 839,927,988 radial basis function, 839 polynomial based approximation, 927,988 artificial neural network models 317,566,743,815,879,902,…”
Section: Introductionmentioning
confidence: 99%
“…,12,18,25,28,34,38,42,47,49,59,84,122,123, 127-129,131,143,145,146,156,165,200,222,225,227,238,240,256,258,261, 265,269,272,282,283,293,315,344,351,360,403,410,412,413,419,422,423, 437,469,470,476,498,500,503,520,530,552,563,565,568,583,592,595,607, 615,620,632,640,641,674,665,724,726,741,745,761,765,771,793,802,812, 848,858,860,861,876,884,911,913,915,923,944,965,970,977,992,1004 twist angle, 217,741,913 curvatures, 324,588 laminate strain components, " xx , " yy , or xx , 192,226,404,491,495,637,717,727, 761,836,859,897,912,922,981 strain energy,820,876,953 or a lower limit on extensional stiffness terms, A ij ,17,35,113,198,317,543,554,589 transverse shear stiffness,113,925 torsional and/or bending stiffness,62,71,81,93,96,105,106,176,178, 230,274,292,554,747,825 axial stiffness,566,947 prebuckling and/or postbuckling stiffness,686 elastic moduli,86,95,110, 117,162,291,377,378,386,408,424,431,514,545,747,1002 stiffness coefficients, 427 bulk modulus,…”
mentioning
confidence: 99%
See 1 more Smart Citation
“…Locating the globally optimal material design with a local search algorithm is almost a hopeless enterprise. For this reason, many researchers preferred global search algorithms like genetic algorithms (Soremekun et al, 2001;Todoroki & Tetsuya, 2004;Kang & Kim, 2005), simulated annealing algorithm (Soares et al, 1995;Jayatheertha et al, 1996;Sciuva et al, 2003;Correia et al, 2003;Erdal & Sonmez, 2005;Moita et al, 2006;Akbulut & Sonmez, 2008), improving hit-and run (Savic et al, 2001). …”
Section: Composites Optimizationmentioning
confidence: 99%