1 \u6982\u7387\u8bba\u4e0e\u6570\u7406\u7edf\u8ba1\u8bcd\u6c47\u82f1\u6c49\u5bf9\u7167\u8868<\/p>\n
A
\nabsolute value \u7edd\u5bf9\u503c
\naccept \u63a5\u53d7
\nacceptable region \u63a5\u53d7\u57df
\nadditivity \u53ef\u52a0\u6027
\nadjusted \u8c03\u6574\u7684
\nalternative hypothesis \u5bf9\u7acb\u5047\u8bbe
\nanalysis \u5206\u6790
\nanalysis of covariance \u534f\u65b9\u5dee\u5206\u6790
\nanalysis of variance \u65b9\u5dee\u5206\u6790
\narithmetic mean \u7b97\u672f\u5e73\u5747\u503c
\nassociation \u76f8\u5173\u6027
\nassumption \u5047\u8bbe
\nassumption checking \u5047\u8bbe\u68c0\u9a8c
\navailability \u6709\u6548\u5ea6
\naverage \u5747\u503c<\/p>\n
B
\nbalanced \u5e73\u8861\u7684
\nband \u5e26\u5bbd
\nbar chart \u6761\u5f62\u56fe
\nbeta-distribution \u8d1d\u5854\u5206\u5e03
\nbetween groups \u7ec4\u95f4\u7684
\nbias \u504f\u501a
\nbinomial distribution \u4e8c\u9879\u5206\u5e03
\nbinomial test \u4e8c\u9879\u68c0\u9a8c<\/p>\n
C
\ncalculate \u8ba1\u7b97
\ncase \u4e2a\u6848
\ncategory \u7c7b\u522b
\ncenter of gravity \u91cd\u5fc3
\ncentral tendency \u4e2d\u5fc3\u8d8b\u52bf
\nchi-square distribution \u5361\u65b9\u5206\u5e03
\nchi-square test \u5361\u65b9\u68c0\u9a8c
\nclassify \u5206\u7c7b
\ncluster analysis \u805a\u7c7b\u5206\u6790
\ncoefficient \u7cfb\u6570
\ncoefficient of correlation \u76f8\u5173\u7cfb\u6570
\ncollinearity \u5171\u7ebf\u6027
\ncolumn \u5217
\ncompare \u6bd4\u8f83
\ncomparison \u5bf9\u7167
\ncomponents \u6784\u6210\uff0c\u5206\u91cf
\ncompound \u590d\u5408\u7684
\nconfidence interval \u7f6e\u4fe1\u533a\u95f4
\nconsistency \u4e00\u81f4\u6027
\nconstant \u5e38\u6570
\ncontinuous variable \u8fde\u7eed\u53d8\u91cf
\ncontrol charts \u63a7\u5236\u56fe
\ncorrelation \u76f8\u5173
\ncovariance \u534f\u65b9\u5dee
\ncovariance matrix \u534f\u65b9\u5dee\u77e9\u9635
\ncritical point \u4e34\u754c\u70b9
\ncritical value \u4e34\u754c\u503c
\ncrosstab \u5217\u8054\u8868
\ncubic \u4e09\u6b21\u7684\uff0c\u7acb\u65b9\u7684
\ncubic term \u4e09\u6b21\u9879
\ncumulative distribution function \u7d2f\u52a0\u5206\u5e03\u51fd\u6570
\ncurve estimation \u66f2\u7ebf\u4f30\u8ba1<\/p>\n
D
\ndata \u6570\u636e
\ndefault \u9ed8\u8ba4\u7684
\ndefinition \u5b9a\u4e49
\ndeleted residual \u5254\u9664\u6b8b\u5dee
\ndensity function \u5bc6\u5ea6\u51fd\u6570
\ndependent variable \u56e0\u53d8\u91cf
\ndescription \u63cf\u8ff0
\ndesign of experiment \u8bd5\u9a8c\u8bbe\u8ba1
\ndeviations \u5dee\u5f02
\ndf.(degree of freedom) \u81ea\u7531\u5ea6
\ndiagnostic \u8bca\u65ad
\ndimension \u7ef4
\ndiscrete variable \u79bb\u6563\u53d8\u91cf
\ndiscriminant function \u5224\u522b\u51fd\u6570
\ndiscriminatory analysis \u5224\u522b\u5206\u6790
\ndistance \u8ddd\u79bb
\ndistribution \u5206\u5e03
\nD-optimal design D-\u4f18\u5316\u8bbe\u8ba1<\/p>\n
E
\neaqual \u76f8\u7b49
\neffects of interaction \u4ea4\u4e92\u6548\u5e94
\nefficiency \u6709\u6548\u6027
\neigenvalue \u7279\u5f81\u503c
\nequal size \u7b49\u542b\u91cf
\nequation \u65b9\u7a0b
\nerror \u8bef\u5dee
\nestimate \u4f30\u8ba1
\nestimation of parameters \u53c2\u6570\u4f30\u8ba1
\nestimations \u4f30\u8ba1\u91cf
\nevaluate \u8861\u91cf
\nexact value \u7cbe\u786e\u503c
\nexpectation \u671f\u671b
\nexpected value \u671f\u671b\u503c
\nexponential \u6307\u6570\u7684
\nexponential distributon \u6307\u6570\u5206\u5e03
\nextreme value \u6781\u503c<\/p>\n
F
\nfactor \u56e0\u7d20\uff0c\u56e0\u5b50
\nfactor analysis \u56e0\u5b50\u5206\u6790
\nfactor score \u56e0\u5b50\u5f97\u5206
\nfactorial designs \u6790\u56e0\u8bbe\u8ba1
\nfactorial experiment \u6790\u56e0\u8bd5\u9a8c
\nfit \u62df\u5408
\nfitted line \u62df\u5408\u7ebf
\nfitted value \u62df\u5408\u503c
\nfixed model \u56fa\u5b9a\u6a21\u578b
\nfixed variable \u56fa\u5b9a\u53d8\u91cf
\nfractional factorial design \u90e8\u5206\u6790\u56e0\u8bbe\u8ba1
\nfrequency \u9891\u6570
\nF-test F\u68c0\u9a8c
\nfull factorial design \u5b8c\u5168\u6790\u56e0\u8bbe\u8ba1
\nfunction \u51fd\u6570<\/p>\n
G
\ngamma distribution \u4f3d\u739b\u5206\u5e03
\ngeometric mean \u51e0\u4f55\u5747\u503c
\ngroup \u7ec4<\/p>\n
H
\nharmomic mean \u8c03\u548c\u5747\u503c
\nheterogeneity \u4e0d\u9f50\u6027
\nhistogram \u76f4\u65b9\u56fe
\nhomogeneity \u9f50\u6027
\nhomogeneity of variance \u65b9\u5dee\u9f50\u6027
\nhypothesis \u5047\u8bbe
\nhypothesis test \u5047\u8bbe\u68c0\u9a8c<\/p>\n
I
\nindependence \u72ec\u7acb
\nindependent variable \u81ea\u53d8\u91cf
\nindependent-samples \u72ec\u7acb\u6837\u672c
\nindex \u6307\u6570
\nindex of correlation \u76f8\u5173\u6307\u6570
\ninteraction \u4ea4\u4e92\u4f5c\u7528
\ninterclass correlation \u7ec4\u5185\u76f8\u5173
\ninterval estimate \u533a\u95f4\u4f30\u8ba1
\nintraclass correlation \u7ec4\u95f4\u76f8\u5173
\ninverse \u5012\u6570\u7684
\niterate \u8fed\u4ee3<\/p>\n
K
\nkernal \u6838
\nKolmogorov-Smirnov test \u67ef\u5c14\u83ab\u54e5\u6d1b\u592b-\u65af\u7c73\u8bfa\u592b\u68c0\u9a8c
\nkurtosis \u5cf0\u5ea6<\/p>\n
L
\nlarge sample problem \u5927\u6837\u672c\u95ee\u9898
\nlayer \u5c42
\nleast-significant difference \u6700\u5c0f\u663e\u8457\u5dee\u6570
\nleast-square estimation \u6700\u5c0f\u4e8c\u4e58\u4f30\u8ba1
\nleast-square method \u6700\u5c0f\u4e8c\u4e58\u6cd5
\nlevel \u6c34\u5e73
\nlevel of significance \u663e\u8457\u6027\u6c34\u5e73
\nleverage value \u4e2d\u5fc3\u5316\u6760\u6746\u503c
\nlife \u5bff\u547d
\nlife test \u5bff\u547d\u8bd5\u9a8c
\nlikelihood function \u4f3c\u7136\u51fd\u6570
\nlikelihood ratio test \u4f3c\u7136\u6bd4\u68c0\u9a8c
\nlinear \u7ebf\u6027\u7684
\nlinear estimator \u7ebf\u6027\u4f30\u8ba1
\nlinear model \u7ebf\u6027\u6a21\u578b
\nlinear regression \u7ebf\u6027\u56de\u5f52
\nlinear relation \u7ebf\u6027\u5173\u7cfb
\nlinear term \u7ebf\u6027\u9879
\nlogarithmic \u5bf9\u6570\u7684
\nlogarithms \u5bf9\u6570
\nlogistic \u903b\u8f91\u7684
\nlost function \u635f\u5931\u51fd\u6570<\/p>\n
M
\nmain effect \u4e3b\u6548\u5e94
\nmatrix \u77e9\u9635
\nmaximum \u6700\u5927\u503c
\nmaximum likelihood estimation \u6781\u5927\u4f3c\u7136\u4f30\u8ba1
\nmean squared deviation(MSD) \u5747\u65b9\u5dee
\nmean sum of square \u5747\u65b9\u548c
\nmeasure \u8861\u91cf
\nmedia \u4e2d\u4f4d\u6570
\nM-estimator M\u4f30\u8ba1
\nminimum \u6700\u5c0f\u503c
\nmissing values \u7f3a\u5931\u503c
\nmixed model \u6df7\u5408\u6a21\u578b
\nmode \u4f17\u6570
\nmodel \u6a21\u578b
\nMonte Carle method \u8499\u7279\u5361\u7f57\u6cd5
\nmoving average \u79fb\u52a8\u5e73\u5747\u503c
\nmulticollinearity \u591a\u5143\u5171\u7ebf\u6027
\nmultiple comparison \u591a\u91cd\u6bd4\u8f83
\nmultiple correlation \u591a\u91cd\u76f8\u5173
\nmultiple correlation coefficient \u590d\u76f8\u5173\u7cfb\u6570
\nmultiple correlation coefficient \u591a\u5143\u76f8\u5173\u7cfb\u6570
\nmultiple regression analysis \u591a\u5143\u56de\u5f52\u5206\u6790
\nmultiple regression equation \u591a\u5143\u56de\u5f52\u65b9\u7a0b
\nmultiple response \u591a\u54cd\u5e94
\nmultivariate analysis \u591a\u5143\u5206\u6790<\/p>\n
N
\nnegative relationship \u8d1f\u76f8\u5173
\nnonadditively \u4e0d\u53ef\u52a0\u6027
\nnonlinear \u975e\u7ebf\u6027
\nnonlinear regression \u975e\u7ebf\u6027\u56de\u5f52
\nnoparametric tests \u975e\u53c2\u6570\u68c0\u9a8c
\nnormal distribution \u6b63\u6001\u5206\u5e03
\nnull hypothesis \u96f6\u5047\u8bbe
\nnumber of cases \u4e2a\u6848\u6570<\/p>\n
O
\none-sample \u5355\u6837\u672c
\none-tailed test \u5355\u4fa7\u68c0\u9a8c
\none-way ANOVA \u5355\u5411\u65b9\u5dee\u5206\u6790
\none-way classification \u5355\u5411\u5206\u7c7b
\noptimal \u4f18\u5316\u7684
\noptimum allocation \u6700\u4f18\u914d\u5236
\norder \u6392\u5e8f
\norder statistics \u6b21\u5e8f\u7edf\u8ba1\u91cf
\norigin \u539f\u70b9
\northogonal \u6b63\u4ea4\u7684
\noutliers \u5f02\u5e38\u503c<\/p>\n
P
\npaired observations \u6210\u5bf9\u89c2\u6d4b\u6570\u636e
\npaired-sample \u6210\u5bf9\u6837\u672c
\nparameter \u53c2\u6570
\nparameter estimation \u53c2\u6570\u4f30\u8ba1
\npartial correlation \u504f\u76f8\u5173
\npartial correlation coefficient \u504f\u76f8\u5173\u7cfb\u6570
\npartial regression coefficient \u504f\u56de\u5f52\u7cfb\u6570
\npercent \u767e\u5206\u6570
\npercentiles \u767e\u5206\u4f4d\u6570
\npie chart \u997c\u56fe
\npoint estimate \u70b9\u4f30\u8ba1
\npoisson distribution \u6cca\u677e\u5206\u5e03
\npolynomial curve \u591a\u9879\u5f0f\u66f2\u7ebf
\npolynomial regression \u591a\u9879\u5f0f\u56de\u5f52
\npolynomials \u591a\u9879\u5f0f
\npositive relationship \u6b63\u76f8\u5173
\npower \u5e42
\nP-P plot P-P\u6982\u7387\u56fe
\npredict \u9884\u6d4b
\npredicted value \u9884\u6d4b\u503c
\nprediction intervals \u9884\u6d4b\u533a\u95f4
\nprincipal component analysis \u4e3b\u6210\u5206\u5206\u6790
\nproability \u6982\u7387
\nprobability density function \u6982\u7387\u5bc6\u5ea6\u51fd\u6570
\nprobit analysis \u6982\u7387\u5206\u6790
\nproportion \u6bd4\u4f8b<\/p>\n
Q
\nqadratic \u4e8c\u6b21\u7684
\nQ-Q plot Q-Q\u6982\u7387\u56fe
\nquadratic term \u4e8c\u6b21\u9879
\nquality control \u8d28\u91cf\u63a7\u5236
\nquantitative \u6570\u91cf\u7684\uff0c\u5ea6\u91cf\u7684
\nquartiles \u56db\u5206\u4f4d\u6570<\/p>\n
R
\nrandom \u968f\u673a\u7684
\nrandom number \u968f\u673a\u6570
\nrandom number \u968f\u673a\u6570
\nrandom sampling \u968f\u673a\u53d6\u6837
\nrandom seed \u968f\u673a\u6570\u79cd\u5b50
\nrandom variable \u968f\u673a\u53d8\u91cf
\nrandomization \u968f\u673a\u5316
\nrange \u6781\u5dee
\nrank \u79e9
\nrank correlation \u79e9\u76f8\u5173
\nrank statistic \u79e9\u7edf\u8ba1\u91cf
\nregression analysis \u56de\u5f52\u5206\u6790
\nregression coefficient \u56de\u5f52\u7cfb\u6570
\nregression line \u56de\u5f52\u7ebf
\nreject \u62d2\u7edd
\nrejection region \u62d2\u7edd\u57df
\nrelationship \u5173\u7cfb
\nreliability \u53ef\u9760\u6027
\nrepeated \u91cd\u590d\u7684
\nreport \u62a5\u544a\uff0c\u62a5\u8868
\nresidual \u6b8b\u5dee
\nresidual sum of squares \u5269\u4f59\u5e73\u65b9\u548c
\nresponse \u54cd\u5e94
\nrisk function \u98ce\u9669\u51fd\u6570
\nrobustness \u7a33\u5065\u6027
\nroot mean square \u6807\u51c6\u5dee
\nrow \u884c
\nrun \u6e38\u7a0b
\nrun test \u6e38\u7a0b\u68c0\u9a8c<\/p>\n
S
\nsample \u6837\u672c
\nsample size \u6837\u672c\u5bb9\u91cf
\nsample space \u6837\u672c\u7a7a\u95f4
\nsampling \u53d6\u6837
\nsampling inspection \u62bd\u6837\u68c0\u9a8c
\nscatter chart \u6563\u70b9\u56fe
\nS-curve S\u5f62\u66f2\u7ebf
\nseparately \u5355\u72ec\u5730
\nsets \u96c6\u5408
\nsign test \u7b26\u53f7\u68c0\u9a8c
\nsignificance \u663e\u8457\u6027
\nsignificance level \u663e\u8457\u6027\u6c34\u5e73
\nsignificance testing \u663e\u8457\u6027\u68c0\u9a8c
\nsignificant \u663e\u8457\u7684\uff0c\u6709\u6548\u7684
\nsignificant digits \u6709\u6548\u6570\u5b57
\nskewed distribution \u504f\u6001\u5206\u5e03
\nskewness \u504f\u5ea6
\nsmall sample problem \u5c0f\u6837\u672c\u95ee\u9898
\nsmooth \u5e73\u6ed1
\nsort \u6392\u5e8f
\nsoruces of variation \u65b9\u5dee\u6765\u6e90
\nspace \u7a7a\u95f4
\nspread \u6269\u5c55
\nsquare \u5e73\u65b9
\nstandard deviation \u6807\u51c6\u79bb\u5dee
\nstandard error of mean \u5747\u503c\u7684\u6807\u51c6\u8bef\u5dee
\nstandardization \u6807\u51c6\u5316
\nstandardize \u6807\u51c6\u5316
\nstatistic \u7edf\u8ba1\u91cf
\nstatistical quality control \u7edf\u8ba1\u8d28\u91cf\u63a7\u5236
\nstd. residual \u6807\u51c6\u6b8b\u5dee
\nstepwise regression analysis \u9010\u6b65\u56de\u5f52
\nstimulus \u523a\u6fc0
\nstrong assumption \u5f3a\u5047\u8bbe
\nstud. deleted residual \u5b66\u751f\u5316\u5254\u9664\u6b8b\u5dee
\nstud. residual \u5b66\u751f\u5316\u6b8b\u5dee
\nsubsamples \u6b21\u7ea7\u6837\u672c
\nsufficient statistic \u5145\u5206\u7edf\u8ba1\u91cf
\nsum \u548c
\nsum of squares \u5e73\u65b9\u548c
\nsummary \u6982\u62ec\uff0c\u7efc\u8ff0<\/p>\n
T
\ntable \u8868
\nt-distribution t\u5206\u5e03
\ntest \u68c0\u9a8c
\ntest criterion \u68c0\u9a8c\u5224\u636e
\ntest for linearity \u7ebf\u6027\u68c0\u9a8c
\ntest of goodness of fit \u62df\u5408\u4f18\u5ea6\u68c0\u9a8c
\ntest of homogeneity \u9f50\u6027\u68c0\u9a8c
\ntest of independence \u72ec\u7acb\u6027\u68c0\u9a8c
\ntest rules \u68c0\u9a8c\u6cd5\u5219
\ntest statistics \u68c0\u9a8c\u7edf\u8ba1\u91cf
\ntesting function \u68c0\u9a8c\u51fd\u6570
\ntime series \u65f6\u95f4\u5e8f\u5217
\ntolerance limits \u5bb9\u8bb8\u9650
\ntotal \u603b\u5171\uff0c\u548c
\ntransformation \u8f6c\u6362
\ntreatment \u5904\u7406
\ntrimmed mean \u622a\u5c3e\u5747\u503c
\ntrue value \u771f\u503c
\nt-test t\u68c0\u9a8c
\ntwo-tailed test \u53cc\u4fa7\u68c0\u9a8c<\/p>\n
U
\nunbalanced \u4e0d\u5e73\u8861\u7684
\nunbiased estimation \u65e0\u504f\u4f30\u8ba1
\nunbiasedness \u65e0\u504f\u6027
\nuniform distribution \u5747\u5300\u5206\u5e03<\/p>\n
V
\nvalue of estimator \u4f30\u8ba1\u503c
\nvariable \u53d8\u91cf
\nvariance \u65b9\u5dee
\nvariance components \u65b9\u5dee\u5206\u91cf
\nvariance ratio \u65b9\u5dee\u6bd4
\nvarious \u4e0d\u540c\u7684
\nvector \u5411\u91cf<\/p>\n
W
\nweight \u52a0\u6743\uff0c\u6743\u91cd
\nweighted average \u52a0\u6743\u5e73\u5747\u503c
\nwithin groups \u7ec4\u5185\u7684<\/p>\n
Z
\nZ score Z\u5206\u6570<\/p>\n
2. \u6700\u4f18\u5316\u65b9\u6cd5\u8bcd\u6c47\u82f1\u6c49\u5bf9\u7167\u8868<\/p>\n
A
\nactive constraint \u6d3b\u52a8\u7ea6\u675f
\nactive set method \u6d3b\u52a8\u96c6\u6cd5
\nanalytic gradient \u89e3\u6790\u68af\u5ea6
\napproximate \u8fd1\u4f3c
\narbitrary \u5f3a\u5236\u6027\u7684
\nargument \u53d8\u91cf
\nattainment factor \u8fbe\u5230\u56e0\u5b50<\/p>\n
B
\nbandwidth \u5e26\u5bbd
\nbe equivalent to \u7b49\u4ef7\u4e8e
\nbest-fit \u6700\u4f73\u62df\u5408
\nbound \u8fb9\u754c<\/p>\n
C
\ncoefficient \u7cfb\u6570
\ncomplex-value \u590d\u6570\u503c
\ncomponent \u5206\u91cf
\nconstant \u5e38\u6570
\nconstrained \u6709\u7ea6\u675f\u7684
\nconstraint \u7ea6\u675f
\nconstraint function \u7ea6\u675f\u51fd\u6570
\ncontinuous \u8fde\u7eed\u7684
\nconverge \u6536\u655b
\ncubic polynomial interpolation method \u4e09\u6b21\u591a\u9879\u5f0f\u63d2\u503c\u6cd5
\ncurve-fitting \u66f2\u7ebf\u62df\u5408<\/p>\n
D
\ndata-fitting \u6570\u636e\u62df\u5408
\ndefault \u9ed8\u8ba4\u7684\uff0c\u9ed8\u8ba4\u7684
\ndefine \u5b9a\u4e49
\ndiagonal \u5bf9\u89d2\u7684
\ndirect search method \u76f4\u63a5\u641c\u7d22\u6cd5
\ndirection of search \u641c\u7d22\u65b9\u5411
\ndiscontinuous \u4e0d\u8fde\u7eed<\/p>\n
E
\neigenvalue \u7279\u5f81\u503c
\nempty matrix \u7a7a\u77e9\u9635
\nequality \u7b49\u5f0f
\nexceeded \u6ea2\u51fa\u7684<\/p>\n
F
\nfeasible \u53ef\u884c\u7684
\nfeasible solution \u53ef\u884c\u89e3
\nfinite-difference \u6709\u9650\u5dee\u5206
\nfirst-order \u4e00\u9636<\/p>\n
G
\nGauss-Newton method \u9ad8\u65af-\u725b\u987f\u6cd5
\ngoal attainment problem \u76ee\u6807\u8fbe\u5230\u95ee\u9898
\ngradient \u68af\u5ea6
\ngradient method \u68af\u5ea6\u6cd5<\/p>\n
H<\/p>\n
handle \u53e5\u67c4
\nHessian matrix \u6d77\u8272\u77e9\u9635<\/p>\n
I
\nindependent variables \u72ec\u7acb\u53d8\u91cf
\ninequality \u4e0d\u7b49\u5f0f
\ninfeasibility \u4e0d\u53ef\u884c\u6027
\ninfeasible \u4e0d\u53ef\u884c\u7684
\ninitial feasible solution \u521d\u59cb\u53ef\u884c\u89e3
\ninitialize \u521d\u59cb\u5316
\ninverse \u9006
\ninvoke \u6fc0\u6d3b
\niteration \u8fed\u4ee3
\niteration \u8fed\u4ee3<\/p>\n
J
\nJacobian \u96c5\u53ef\u6bd4\u77e9\u9635<\/p>\n
L
\nLagrange multiplier \u62c9\u683c\u6717\u65e5\u4e58\u5b50
\nlarge-scale \u5927\u578b\u7684
\nleast square \u6700\u5c0f\u4e8c\u4e58
\nleast squares sense \u6700\u5c0f\u4e8c\u4e58\u610f\u4e49\u4e0a\u7684
\nLevenberg-Marquardt method \u5217\u6587\u4f2f\u683c-\u9a6c\u5938\u5c14\u7279\u6cd5
\nline search \u4e00\u7ef4\u641c\u7d22
\nlinear \u7ebf\u6027\u7684
\nlinear equality constraints \u7ebf\u6027\u7b49\u5f0f\u7ea6\u675f
\nlinear programming problem \u7ebf\u6027\u89c4\u5212\u95ee\u9898
\nlocal solution \u5c40\u90e8\u89e3<\/p>\n
M
\nmedium-scale \u4e2d\u578b\u7684
\nminimize \u6700\u5c0f\u5316
\nmixed quadratic and cubic polynomial interpolation and extrapolation method \u6df7\u5408\u4e8c\u6b21\u3001\u4e09\u6b21\u591a\u9879\u5f0f\u5185\u63d2\u3001\u5916\u63d2\u6cd5
\nmultiobjective \u591a\u76ee\u6807\u7684<\/p>\n
N
\nnonlinear \u975e\u7ebf\u6027\u7684
\nnorm \u8303\u6570<\/p>\n
O
\nobjective function \u76ee\u6807\u51fd\u6570
\nobserved data \u6d4b\u91cf\u6570\u636e
\noptimization routine \u4f18\u5316\u8fc7\u7a0b
\noptimize \u4f18\u5316
\noptimizer \u6c42\u89e3\u5668
\nover-determined system \u8d85\u5b9a\u7cfb\u7edf<\/p>\n
P
\nparameter \u53c2\u6570
\npartial derivatives \u504f\u5bfc\u6570
\npolynomial interpolation method \u591a\u9879\u5f0f\u63d2\u503c\u6cd5<\/p>\n
Q
\nquadratic \u4e8c\u6b21\u7684
\nquadratic interpolation method \u4e8c\u6b21\u5185\u63d2\u6cd5
\nquadratic programming \u4e8c\u6b21\u89c4\u5212<\/p>\n
R
\nreal-value \u5b9e\u6570\u503c
\nresiduals \u6b8b\u5dee
\nrobust \u7a33\u5065\u7684
\nrobustness \u7a33\u5065\u6027\uff0c\u9c81\u68d2\u6027<\/p>\n
S
\nscalar \u6807\u91cf
\nsemi-infinitely problem \u534a\u65e0\u9650\u95ee\u9898
\nSequential Quadratic Programming method \u5e8f\u5217\u4e8c\u6b21\u89c4\u5212\u6cd5
\nsimplex search method \u5355\u7eaf\u5f62\u6cd5
\nsolution \u89e3
\nsparse matrix \u7a00\u758f\u77e9\u9635
\nsparsity pattern \u7a00\u758f\u6a21\u5f0f
\nsparsity structure \u7a00\u758f\u7ed3\u6784
\nstarting point \u521d\u59cb\u70b9 stationary point \u9a7b\u70b9
\nstep length \u6b65\u957f
\nsubspace trust region method \u5b50\u7a7a\u95f4\u7f6e\u4fe1\u57df\u6cd5
\nsum-of-squares \u5e73\u65b9\u548c
\nsymmetric matrix \u5bf9\u79f0\u77e9\u9635<\/p>\n
T
\ntermination message \u7ec8\u6b62\u4fe1\u606f
\ntermination tolerance \u7ec8\u6b62\u5bb9\u9650
\nthe exit condition \u9000\u51fa\u6761\u4ef6
\nthe method of steepest descent \u6700\u901f\u4e0b\u964d\u6cd5
\ntranspose \u8f6c\u7f6e<\/p>\n
U
\nunconstrained \u65e0\u7ea6\u675f\u7684
\nunder-determined system \u8d1f\u5b9a\u7cfb\u7edf<\/p>\n
V
\nvariable \u53d8\u91cf
\nvector \u77e2\u91cf<\/p>\n
W
\nweighting matrix \u52a0\u6743\u77e9\u9635<\/p>\n
3 \u6837\u6761\u8bcd\u6c47\u82f1\u6c49\u5bf9\u7167\u8868<\/p>\n
A
\napproximation \u903c\u8fd1
\narray \u6570\u7ec4
\na spline in b-form\/b-spline b\u6837\u6761
\na spline of polynomial piece \/ppform spline \u5206\u6bb5\u591a\u9879\u5f0f\u6837\u6761<\/p>\n
B
\nbivariate spline function \u4e8c\u5143\u6837\u6761\u51fd\u6570
\nbreak\/breaks \u65ad\u70b9<\/p>\n
C
\ncoefficient\/coefficients \u7cfb\u6570
\ncubic interpolation \u4e09\u6b21\u63d2\u503c\/\u4e09\u6b21\u5185\u63d2
\ncubic polynomial \u4e09\u6b21\u591a\u9879\u5f0f
\ncubic smoothing spline \u4e09\u6b21\u5e73\u6ed1\u6837\u6761
\ncubic spline \u4e09\u6b21\u6837\u6761
\ncubic spline interpolation \u4e09\u6b21\u6837\u6761\u63d2\u503c\/\u4e09\u6b21\u6837\u6761\u5185\u63d2
\ncurve \u66f2\u7ebf<\/p>\n
D
\ndegree of freedom \u81ea\u7531\u5ea6
\ndimension \u7ef4\u6570<\/p>\n
E
\nend conditions \u7ea6\u675f\u6761\u4ef6<\/p>\n
I
\ninput argument \u8f93\u5165\u53c2\u6570
\ninterpolation \u63d2\u503c\/\u5185\u63d2
\ninterval \u53d6\u503c\u533a\u95f4<\/p>\n
K
\nknot\/knots \u8282\u70b9<\/p>\n
L
\nleast-squares approximation \u6700\u5c0f\u4e8c\u4e58\u62df\u5408<\/p>\n
M
\nmultiplicity \u91cd\u6b21
\nmultivariate function \u591a\u5143\u51fd\u6570<\/p>\n
O
\noptional argument \u53ef\u9009\u53c2\u6570
\norder \u9636\u6b21
\noutput argument \u8f93\u51fa\u53c2\u6570<\/p>\n
P
\npoint\/points \u6570\u636e\u70b9<\/p>\n
R
\nrational spline \u6709\u7406\u6837\u6761
\nrounding error \u820d\u5165\u8bef\u5dee\uff08\u76f8\u5bf9\u8bef\u5dee\uff09<\/p>\n
S
\nscalar \u6807\u91cf
\nsequence \u6570\u5217\uff08\u6570\u7ec4\uff09
\nspline \u6837\u6761
\nspline approximation \u6837\u6761\u903c\u8fd1\/\u6837\u6761\u62df\u5408
\nspline function \u6837\u6761\u51fd\u6570
\nspline curve \u6837\u6761\u66f2\u7ebf
\nspline interpolation \u6837\u6761\u63d2\u503c\/\u6837\u6761\u5185\u63d2
\nspline surface \u6837\u6761\u66f2\u9762
\nsmoothing spline \u5e73\u6ed1\u6837\u6761<\/p>\n
T
\ntolerance \u5141\u8bb8\u7cbe\u5ea6<\/p>\n
U
\nunivariate function \u4e00\u5143\u51fd\u6570<\/p>\n
V
\nvector \u5411\u91cf<\/p>\n
W
\nweight\/weights \u6743\u91cd<\/p>\n
4 \u504f\u5fae\u5206\u65b9\u7a0b\u6570\u503c\u89e3\u8bcd\u6c47\u82f1\u6c49\u5bf9\u7167\u8868<\/p>\n
A
\nabsolute error \u7edd\u5bf9\u8bef\u5dee
\nabsolute tolerance \u7edd\u5bf9\u5bb9\u9650
\nadaptive mesh \u9002\u5e94\u6027\u7f51\u683c<\/p>\n
B
\nboundary condition \u8fb9\u754c\u6761\u4ef6<\/p>\n
C
\ncontour plot \u7b49\u503c\u7ebf\u56fe
\nconverge \u6536\u655b
\ncoordinate \u5750\u6807\u7cfb<\/p>\n
D
\ndecomposed \u5206\u89e3\u7684
\ndecomposed geometry matrix \u5206\u89e3\u51e0\u4f55\u77e9\u9635
\ndiagonal matrix \u5bf9\u89d2\u77e9\u9635
\nDirichlet boundary conditions
\nDirichlet\u8fb9\u754c\u6761\u4ef6<\/p>\n
E
\neigenvalue \u7279\u5f81\u503c
\nelliptic \u692d\u5706\u5f62\u7684
\nerror estimate \u8bef\u5dee\u4f30\u8ba1
\nexact solution \u7cbe\u786e\u89e3<\/p>\n
G
\ngeneralized Neumann boundary condition \u63a8\u5e7f\u7684Neumann\u8fb9\u754c\u6761\u4ef6
\ngeometry \u51e0\u4f55\u5f62\u72b6
\ngeometry description matrix \u51e0\u4f55\u63cf\u8ff0\u77e9\u9635
\ngeometry matrix \u51e0\u4f55\u77e9\u9635
\ngraphical user interface\uff08GUI\uff09 \u56fe\u5f62\u7528\u6237\u754c\u9762<\/p>\n
H
\nhyperbolic \u53cc\u66f2\u7ebf\u7684<\/p>\n
I
\ninitial mesh \u521d\u59cb\u7f51\u683c<\/p>\n
J
\njiggle \u5fae\u8c03<\/p>\n
L
\nLagrange multipliers \u62c9\u683c\u6717\u65e5\u4e58\u5b50
\nLaplace equation \u62c9\u666e\u62c9\u65af\u65b9\u7a0b
\nlinear interpolation \u7ebf\u6027\u63d2\u503c
\nloop \u5faa\u73af<\/p>\n
M
\nmachine precision \u673a\u5668\u7cbe\u5ea6
\nmixed boundary condition \u6df7\u5408\u8fb9\u754c\u6761\u4ef6<\/p>\n
N
\nNeuman boundary condition Neuman\u8fb9\u754c\u6761\u4ef6
\nnode point \u8282\u70b9
\nnonlinear solver \u975e\u7ebf\u6027\u6c42\u89e3\u5668
\nnormal vector \u6cd5\u5411\u91cf<\/p>\n
P
\nParabolic \u629b\u7269\u7ebf\u578b\u7684
\npartial differential equation \u504f\u5fae\u5206\u65b9\u7a0b
\nplane strain \u5e73\u9762\u5e94\u53d8
\nplane stress \u5e73\u9762\u5e94\u529b
\nPoisson’s equation \u6cca\u677e\u65b9\u7a0b
\npolygon \u591a\u8fb9\u5f62
\npositive definite \u6b63\u5b9a<\/p>\n
Q
\nquality \u8d28\u91cf<\/p>\n
R
\nrefined triangular mesh \u52a0\u5bc6\u7684\u4e09\u89d2\u5f62\u7f51\u683c
\nrelative tolerance \u76f8\u5bf9\u5bb9\u9650
\nrelative tolerance \u76f8\u5bf9\u5bb9\u9650
\nresidual \u6b8b\u5dee
\nresidual norm \u6b8b\u5dee\u8303\u6570<\/p>\n
S
\nsingular \u5947\u5f02\u7684<\/p>\n","protected":false},"excerpt":{"rendered":"
1 \u6982\u7387\u8bba\u4e0e\u6570\u7406\u7edf\u8ba1\u8bcd\u6c47\u82f1\u6c49\u5bf9\u7167\u8868 A absolute value \u7edd\u5bf9\u503c accept \u63a5\u53d7 acceptable region \u63a5\u53d7\u57df additivit…<\/p>\n