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UMMDp180718

于 2021-03-22 发布文件大小:59KB

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下载积分: 1 下载次数: 6

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代码说明：

新的屈服准则ABAQUS子程序UMAT，含有多种较新的准侧，能很好运用(The new yield criterion ABAQUS subroutine UMAT contains a variety of newer standards and can be used very well.)

文件列表：

UMMDp_180718\ummdp.f, 38272 , 2018-06-30
UMMDp_180718\ummdp_curve.f, 5331 , 2018-06-30
UMMDp_180718\ummdp_kinematic.f, 15433 , 2017-09-06
UMMDp_180718\ummdp_setprop.f, 20683 , 2018-06-30
UMMDp_180718\ummdp_utl.f, 18315 , 2017-09-06
UMMDp_180718\ummdp_yfunc_bbc2005.f, 8647 , 2017-09-06
UMMDp_180718\ummdp_yfunc_bbc2008.f, 18831 , 2017-09-06
UMMDp_180718\ummdp_yfunc_cazacu2006.f, 18463 , 2017-09-06
UMMDp_180718\ummdp_yfunc_control.f, 10377 , 2017-09-06
UMMDp_180718\ummdp_yfunc_gotoh.f, 3238 , 2017-09-06
UMMDp_180718\ummdp_yfunc_hill48.f, 2018 , 2017-09-06
UMMDp_180718\ummdp_yfunc_hill90.f, 10190 , 2017-09-06
UMMDp_180718\ummdp_yfunc_hu2005.f, 1591 , 2017-09-06
UMMDp_180718\ummdp_yfunc_karafillisboyce.f, 17895 , 2017-12-29
UMMDp_180718\ummdp_yfunc_mises.f, 1211 , 2017-09-06
UMMDp_180718\ummdp_yfunc_vegter.f, 49042 , 2017-12-29
UMMDp_180718\ummdp_yfunc_yld2000.f, 12482 , 2017-09-06
UMMDp_180718\ummdp_yfunc_yld2004.f, 23998 , 2018-07-07
UMMDp_180718\ummdp_yfunc_yld89.f, 7737 , 2017-09-06
UMMDp_180718\ummdp_yfunc_yoshida2011.f, 6145 , 2017-09-06

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2021-01-04 15:58:55下载

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2019-03-12 10:37:54下载

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常规波束形成与最小方差无畸变算法性能对比（CBF、MVDR）(Performance comparison between conventional beamforming and minimum variance distortionless algorithm CBF MVDR)

2020-11-04 21:19:52下载

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2021-01-17 22:38:44下载

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2020-03-16 19:17:40下载

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说明：蚁群算法用来函数寻优，本代码给出了通用版本，能够很好的适应和学习(Ant colony algorithm is used to function optimization, this code gives a general version, can adapt and learn well)

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说明：实现多种二维面阵的综合设计，包括均匀阵、切比雪夫阵等等。。。。(Comprehensive design of various 2D arrays)

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汽车动力模型，包括：整车模型，发动机模型，ECU模型，变速器模型等。(Basic example : Gasoline vehicle)

2020-07-03 03:00:02下载

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大坝变形的精确预测对其安全评价方面发挥着重要作用。为了进一步提高大坝变形预测精度，提出了一种改进的灰狼算法优化支持向量机的预测模型。通过引入非线性收敛因子和采用动态加权策略，提升灰狼算法优化支持向量机惩罚因子和核函数参数的能力，并以优化的参数来建立支持向量机大坝变形预测模型。选取实例数据，在与布谷鸟算法、差分进化算法、粒子群算法、基本灰狼算法优化的支持向量机预测比较中，实验结果表明，改进的灰狼算法对支持向量机参数的优化是有效的，可以提高大坝变形预测精度。(Accurate prediction of dam deformation plays an important role in its safety evaluation. In order to further improve the prediction accuracy of dam deformation, an improved grey Wolf algorithm is proposed to optimize the prediction model of support vector machine. By introducing the nonlinear convergence factor and using the dynamic weighting strategy, the gray wolf algorithm is improved to optimize the capacity of the support vector machine penalty factor and the kernel parameter, and the support vector machine dam deformation prediction model is established with the optimized parameters. The experimental results show that the Improved Grey Wolf algorithm is effective for the optimization of support vector machine parameters, and it can improve the accuracy of the deformation prediction of the dam.)

2018-06-28 09:53:07下载

积分：1

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