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minFunc_2012

于 2020-01-13 发布
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下载积分: 1 下载次数: 6
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代码说明：

说明：  集成了梯度下降法、牛顿法、拟牛顿法、共轭梯度法等多个最优化算法(It integrates gradient descent method, Newton method, quasi Newton method, conjugate gradient method and other optimization algorithms)

文件列表：

minFunc_2012\autoDif\autoGrad.m, 1319 , 2012-03-27
minFunc_2012\autoDif\autoHess.m, 1184 , 2012-03-27
minFunc_2012\autoDif\autoHv.m, 371 , 2011-01-05
minFunc_2012\autoDif\autoTensor.m, 1338 , 2012-03-17
minFunc_2012\autoDif\derivativeCheck.m, 1034 , 2012-03-27
minFunc_2012\autoDif\fastDerivativeCheck.m, 1704 , 2012-03-27
minFunc_2012\example_derivativeCheck.m, 1392 , 2012-03-27
minFunc_2012\example_minFunc.m, 2518 , 2019-12-20
minFunc_2012\logisticExample\example_minFunc_LR.m, 2449 , 2012-03-27
minFunc_2012\logisticExample\LogisticDiagPrecond.m, 417 , 2011-01-05
minFunc_2012\logisticExample\LogisticHv.m, 216 , 2011-01-05
minFunc_2012\logisticExample\LogisticLoss.m, 709 , 2012-02-08
minFunc_2012\logisticExample\mylogsumexp.m, 228 , 2011-01-05
minFunc_2012\mexAll.m, 350 , 2019-06-11
minFunc_2012\minFunc\ArmijoBacktrack.m, 4533 , 2011-01-05
minFunc_2012\minFunc\compiled\lbfgsAddC.mexa64, 8264 , 2012-03-17
minFunc_2012\minFunc\compiled\lbfgsAddC.mexmaci64, 8696 , 2019-06-11
minFunc_2012\minFunc\compiled\lbfgsAddC.mexw64, 7680 , 2019-12-20
minFunc_2012\minFunc\compiled\lbfgsC.mexa64, 7707 , 2011-01-05
minFunc_2012\minFunc\compiled\lbfgsC.mexglx, 7733 , 2011-01-05
minFunc_2012\minFunc\compiled\lbfgsC.mexmac, 9500 , 2011-01-05
minFunc_2012\minFunc\compiled\lbfgsC.mexmaci, 12660 , 2011-01-05
minFunc_2012\minFunc\compiled\lbfgsC.mexmaci64, 8844 , 2019-06-11
minFunc_2012\minFunc\compiled\lbfgsC.mexw32, 7168 , 2011-01-05
minFunc_2012\minFunc\compiled\lbfgsC.mexw64, 9728 , 2019-12-20
minFunc_2012\minFunc\compiled\lbfgsProdC.mexa64, 12498 , 2012-03-17
minFunc_2012\minFunc\compiled\lbfgsProdC.mexmaci64, 12968 , 2019-06-11
minFunc_2012\minFunc\compiled\lbfgsProdC.mexw64, 10240 , 2019-12-20
minFunc_2012\minFunc\compiled\mcholC.mexa64, 12701 , 2011-03-10
minFunc_2012\minFunc\compiled\mcholC.mexglx, 13046 , 2011-01-05
minFunc_2012\minFunc\compiled\mcholC.mexmac, 9936 , 2011-01-05
minFunc_2012\minFunc\compiled\mcholC.mexmaci64, 13220 , 2019-06-11
minFunc_2012\minFunc\compiled\mcholC.mexw32, 8192 , 2011-01-05
minFunc_2012\minFunc\compiled\mcholC.mexw64, 11776 , 2019-12-20
minFunc_2012\minFunc\conjGrad.m, 1886 , 2011-01-05
minFunc_2012\minFunc\dampedUpdate.m, 996 , 2018-02-28
minFunc_2012\minFunc\isLegal.m, 107 , 2011-01-05
minFunc_2012\minFunc\lbfgs.m, 924 , 2011-01-05
minFunc_2012\minFunc\lbfgsAdd.m, 679 , 2012-03-17
minFunc_2012\minFunc\lbfgsProd.m, 696 , 2012-03-17
minFunc_2012\minFunc\lbfgsUpdate.m, 614 , 2012-02-07
minFunc_2012\minFunc\mchol.m, 1308 , 2011-01-05
minFunc_2012\minFunc\mcholinc.m, 564 , 2011-01-05
minFunc_2012\minFunc\mex\lbfgsAddC.c, 802 , 2012-03-17
minFunc_2012\minFunc\mex\lbfgsC.c, 2408 , 2011-01-05
minFunc_2012\minFunc\mex\lbfgsProdC.c, 2285 , 2012-03-17
minFunc_2012\minFunc\mex\mcholC.c, 4192 , 2011-01-05
minFunc_2012\minFunc\minFunc.m, 42735 , 2012-03-27
minFunc_2012\minFunc\minFunc_processInputOptions.m, 4103 , 2012-02-07
minFunc_2012\minFunc\polyinterp.m, 4158 , 2012-11-06
minFunc_2012\minFunc\precondDiag.m, 42 , 2011-01-05
minFunc_2012\minFunc\precondTriu.m, 51 , 2011-01-05
minFunc_2012\minFunc\precondTriuDiag.m, 60 , 2011-01-05
minFunc_2012\minFunc\taylorModel.m, 677 , 2011-01-05
minFunc_2012\minFunc\WolfeLineSearch.m, 10590 , 2012-04-04
minFunc_2012\rosenbrock.m, 812 , 2016-04-16
minFunc_2012\minFunc\compiled, 0 , 2019-12-20
minFunc_2012\minFunc\mex, 0 , 2019-11-18
minFunc_2012\autoDif, 0 , 2019-11-18
minFunc_2012\logisticExample, 0 , 2019-11-18
minFunc_2012\minFunc, 0 , 2019-11-18
minFunc_2012, 0 , 2019-11-25

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    为了提高三级倒立摆系统控制的响应速度和稳定性,在设计Mamdani 型模糊推理规则控制器控制倒立摆系统稳定的基础上, 设计了一种更有效率的基于Sugeno 型模糊推理规则的模糊神经网络控制器。该控制器使用BP 神经网络和最小二乘法的混 合算法进行参数训练,能够准确归纳输入输出量的模糊隶属度函数和模糊逻辑规则。通过与Mamdani 型控制器的仿真对比, 表明该Sugeno 型模糊神经网络控制器对三级倒立摆系统的控制具有良好的稳定性和快速性,以及较高的控制精度。(In order to improve the three-level control of inverted pendulum system response speed and stability, in the design of Mamdani-type fuzzy inference rules of the system controller to control the stability of inverted pendulum on the basis of a more efficient design based on Sugeno-type fuzzy inference rules of fuzzy neural network controller. The controller is the use of BP neural network and hybrid least squares training algorithm parameters can be accurately summed up the amount of input and output fuzzy membership function and fuzzy logic rules. Mamdani-type controller with a simulation comparison shows that the Sugeno-type fuzzy neural network controller for the three-tier control of inverted pendulum system with good stability and fast, as well as a higher control precision.)
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