TRAINING的案例
FLOTHERM V6.1 MCAD training
FLOTHERM V6.1 MCAD training
FloMcad training.part01.rar
FloMcad training.part02.rar
FloMcad training.part03.rar
FloMcad training.part04.rar
FloMcad training.part05.rar
eta/DYNAFORM Basic Training Manual
DYNAFORM Basic Training for Stamping CAE Engineers
DYNAFORM Basic Training.part1.rar
DYNAFORM Basic Training.part2.rar
DYNAFORM Basic Training.part3.rar
【資源帖】CAD-CAE_Training 香港理工大學的全套英文教程，需要的朋友可以自行下載查看。
CAD-CAE_Training 香港理工大學的全套英文教程，
需要的朋友可以自行下載查看。
香港理工大學CAD-CAE_Training_Centre的全套教程_V5KWA_1.pdf
香港理工大學CAD-CAE_Training_Centre的全套教程_V5F_Ver1.pdf
香港理工大學CAD-CAE_Training_Centre的全套教程_Tut05-perfume.pdf
Mentat_Training_Manual. mentat前處理使用手冊
Mentat_Training_Manual.
Mentat_Training_Manual.part1.rar
Mentat_Training_Manual.part2.rar
Basic ADAMS Full Simulation Training&nbs
1/3
Basic ADAMS Full Simulation Training Guide.part1.rar
Basic ADAMS Full Simulation Training Guide.part2.rar
Basic ADAMS Full Simulation Training Guide.part3.rar
『分享』ADAMS Basic Training（臺灣 廖偉志）
ADAMS Basic Training（臺灣 廖偉志）.part1.rar
ADAMS Basic Training（臺灣 廖偉志）.part2.rar
ADAMS Basic Training（臺灣 廖偉志）.part3.rar
ANSA training
ANSA_training_1st_day.pdf
ANSA_training_2nd_day.pdf
[下載]Basic ADAMS Solver Training Guide
兩個文件
Basic ADAMS Solver Training Guide.part1.rar
Basic ADAMS Solver Training Guide.part2.rar
ADAMS_Flex_Training_Guide
共有2個壓縮包 no.1
ADAMS_Flex_Training_Guide.part1.rar
ADAMS_Flex_Training_Guide.part2.rar
『分享』ADAMS Flex Training Guide
經典FLEX指南不錯的資料
ADAMS Flex Training Guide.part1.rar
ADAMS Flex Training Guide.part2.rar
DynaForm 5.8 BSE_Training_Tutorial坯料工程培訓手冊中文版
DynaForm 5.8 BSE_Training_Tutorial坯料工程培訓手冊中文版
帶培訓所需模型文件。
軟件安裝后，安裝目錄的manual文件夾里有，這個是中文版的。（如果有中文版dyanform5.8的請路過。。。）
BSE_Training_Tutorial_CHS.rar
極限學習機matlab實戰
1、辛烷值的預測
clear all
clc
%% 訓練集/測試集產生
load spectra_data.mat
% 隨機產生訓練集和測試集
temp = randperm(size(NIR,1));
% 訓練集——50個樣本
P_train = NIR(temp(1:50),:)';
T_train = octane(temp(1:50),:)';
% 測試集——10個樣本
P_test = NIR(temp(51:end),:)';
T_test = octane(temp(51:end),:)';
N = size(P_test,2);
%% 數據歸一化
% 訓練集
[Pn_train,inputps] = mapminmax(P_train);
Pn_test = mapminmax('apply',P_test,inputps);
% 測試集
[Tn_train,outputps] = mapminmax(T_train);
Tn_test = mapminmax('apply',T_test,outputps);
%% ELM創建/訓練
[IW,B,LW,TF,TYPE] = elmtrain(Pn_train,Tn_train,30,'sig',0);
%% ELM仿真測試
tn_sim = elmpredict(Pn_test,IW,B,LW,TF,TYPE);
% 反歸一化
T_sim = mapminmax('reverse',tn_sim,outputps);
%% 結果對比
result = [T_test' T_sim'];
% 均方誤差
E = mse(T_sim - T_test
展開 sklearn中決策樹的應用（python）
(train_x,train_y)
classi.score(test_x,test_y)
Out[136]: 0.9333333333333333
classi=DecisionTreeClassifier(splitter='best')
classi.fit(train_x,train_y)
classi.score(test_x,test_y)
Out[139]: 0.9666666666666667
classi=DecisionTreeClassifier(splitter='random')
classi.fit(train_x,train_y)
classi.score(test_x,test_y)
Out[142]: 0.8666666666666667
scor=[]
for i in range(1,20):
classi=DecisionTreeClassifier(max_depth=i)
classi.fit(train_x,train_y)
sco=classi.score(test_x,test_y)
scor.append(sco)
plt.plot(range(1,20),scor)
plt.grid(axis='both')
03 構造數據
import numpy as np
import matplotlib.pyplot as plt
from sklearn.tree import DecisionTreeRegressor
x=np.pi*2*np.random.rand(100)
y=np.sin(x)
y[::5]+=(np.random.rand(20)-0.5)*2
dex1=np.random.choice(100,75,
展開 sklearn中支持向量機（SVM）用于分類
='linear',max_iter=-1)
classi.fit(train_x,train_y)
print(classi.coef_)
print(classi.intercept_)
print(classi.score(test_x,test_y))
[[-0.04617041 0.52139469 -1.00309152 -0.46414917]
[-0.00709388 0.17889062 -0.53842766 -0.29225126]
[ 0.41433436 0.33921135 -2.05263433 -1.87171831]]
[1.45194667 1.50728785 9.70602451]
0.9666666666666667
在kernel=‘‘poly’’下，考察degree，gamma，coef0對模型預測性能的影響：
degrees=range(1,20)
scor=[]
for degree in degrees:
classi=svm.SVC(kernel='poly',degree=degree,gamma='auto')
classi.fit(train_x,train_y)
scor.append(classi.score(test_x,test_y))
plt.plot(degrees,scor)
plt.ylim(0,1.1）
gammas=range(1,20)
scor=[]
for gamma in gammas:
classi=svm.SVC(kernel='poly',degree=3,gamma=gamma)
classi.fit(train_x,train_y)
scor.append(classi.score(test_x,test_y
展開 sklearn中K近鄰的使用（python）
i in range(1797):
if i not in dex1:
dex2.append(i)
train_x=digits.data[dex1]
train_y=digits.target[dex1]
test_x=digits.data[dex2]
test_y=digits.target[dex2]
03 KNN分類
classi=neighbors.KNeighborsClassifier()
classi.fit(train_x,train_y)
classi.score(test_x,test_y)
Out[120]: 0.9966329966329966
研究參數weights，n_neighbors，p對模型預測性能的影響：
fig=plt.figure()
weights=['uniform','distance']
ks=np.linspace(1,len(train_y),100,dtype='int')
for weight in weights:
scor=[]
for k in ks:
classi=neighbors.KNeighborsClassifier(weights=weight,n_neighbors=k)
classi.fit(train_x,train_y)
scor.append(classi.score(test_x,test_y))
plt.plot(ks,scor,label=weight)
plt.legend(loc='best')
fig=plt.figure()
ps=[1,2,10]
ks=np.linspace(1,len(train_y),100,dtype='int')
展開 