Main Services

   ◆  make modeling more dimensions (more than five-dimensional) data and other special data regression.

   ◆  According to the user's requirements, the mechanism model , added to the software "DRS" in.

   ◆  Develop data regression analysis components (.Dll and .Ocx file), to meet the user needs of software development.

   ◆  undertake data analysis and its corresponding programming algorithm.

   ◆  undertake the preparation of data regression classes and class engineering calculation software.

 

            Two dimensions data model se rvice demo  
            Three dimensions data model service demo 
            Four  dimensions data model service demo

 

Two dimensions data model service demo

 

The relating Two dimensions data, as Curve chart 1-1

   

From the data checked in the curve chart 1-1 , the conclusions of the recursive model are as follows:

The data return

1.0,8.2
2.0,4.6
3.0,4.3
4.0,4.6
5.0,5.1
6.0,5.5
7.0,5.7
8.0,5.5
9.0,5.0
10.0,3.8

 

 

The result of data return

   

To the above data ,  the enhancement edition of the data regression analysis system DRSis used by you.
You do the one element regression in which the sample quantity of the regression data is 10 groups.
The corresponding data in the model chosen by you are the objective function Y, which are the second dimensional data in each group and the first dimensional data X1.
Set initial value of the power to .49. 
At present,  the best model to regress these data is  the one element nonlinear, three variant

Correlation coefficient(R): 0.998440262665158
Statistical variable(F): 639.633664360148
Residue standard deviation(S): 8.20568908339411E-02
Biggest error: 0.100178925962695681378721421
Equal error: 0.0434778
Equal relating error: 0.0094209513911285916313423627                   
The recursive models are as follows
 

y = a0 + a1 x1k1 + a2  x1k2 + a3  x1k3
 

Formula middle

a0 = 53.0773524156633
a1 = -111.002257397679
a2 = 68.6071595939835
a3 = -2.48944355659807

k1 = .470000009536
k2 = .710000009536
k3 = 1.470000009536

The demo of three data recursive model
 

The relating three dimensions data, as Curve chart 2-2

   

From the data checked in the curve chart 2-2, the conclusions of the recursive model are as follows:                                           
 

The data return

10,20,90
10,40,178
10,60,270
10,80,350
10,100,430
10,120,515

..............(In the middle the data ellipsis)

70,200,990
80,100,920
80,120,955
80,140,1020
80,160,1055
80,180,1090
80,200,1120

 

The result of data return

To the above data ,  the enhancement edition of the data regression analysis system DRSis used by you. 
You do the one element regression in which the sample quantity of the regression data is 72 groups. 
The corresponding data in the model chosen by you are the objective function Y, which are the three  dimensional data in each group and The first dimensional data X1 and the second dimensional data X2.
Set initial value of the power to  1.871. 

At present, the best model to regress these data is the one element nonlinear, three variant .

Correlation coefficient(R):0.999391761013444
Statistical variable(F):8897.36657079412
Residue standard deviation(S): 10.0200437606299
Biggest error: 3.309665
Equal error: 0.0434778
Equal relating error:.0211348280652982339123964839

The recursive models are as follows

y = a0 + a1  x1k1 + a2 x1k2 + a3  x2 k1 + a4  x2 k3 + a5  x1k4 x2 k5 + a6  x1k6 Log(x2)

Formula middle 

a0 = 106.74503499072
a1 = -10.5248008279283  
a2 = .174240332503716 
a3 = 1.7623076630686 
a4 = 2.68381176972421   
a5 = -3.21815581782274E-06 
a6 = -.511646453024215

k1 = .991000051498
k2 = 2.041000051498
k3 = .991000051498
k4 = 1.881000051498
k5 = 1.881000051498
k6 = .781000051498

 The demo of four data recursive model  

Four  dimensions data:

65.7, 20, 56, 2.8
65.2, 20, 53, 2.8
65.4, 10, 52, 2.8
66.7, 10, 48, 2.8
67.2, 10, 47, 2.8
71.4, 10, 45, 2.8
73.4, 10, 45, 2.8
..............(In the middle the data ellipsis)

87.8, 10, 9, 1.9
89.6, 10, 8, 1.9
88.6, 10, 8, 1.9
89.6, 10, 7, 1.9
88.8, 10, 6, 1.8
88.6, 10, 6, 1.8

The result of data return

To the above data , the enhancement edition of the ‘data regression analysis system DRS’is used by you. 
You do the one element regression in which the sample quantity of the regression data is 84 groups. 
The corresponding data in the model chosen by you are the objective function Y, which are the three dimensional data in each group and the first dimensional data X1 and the second dimensional data X2 and the three dimensional data X3.
Set initial value of the power to .85.                                    
At present, the best model to regress these data is the one element nonlinear, six variant Ⅳ. 

Correlation coefficient(R): .978284893845566
Statistical variable(F): 285.903438238213

Residue standard deviation(S): 6.52420615708014E-02
Biggest error: .1576

Equal error: 4.943928E-02

Equal relating error: 2.11378593605741E-02

The recursive models are as follows

y = a0 + a1  x1k1 + a2  x2 k2 + a3 x3 k3 + a4  x1k4  x2k5 + a5 x2 k5 * x3 k6 + a6 x1k1 * x2k2  x3k3

Formula middle
a0 = .98681044401583 
a1 = 7.06127446632436E-03 
a2 = .190801629630409 
a3 = 4.10554369554979E-02 
a4 = -8.33265112895352E-03 
a5 = -6.09571087259014E-03 
a6 = 2.28798807062382E-05

 

k1 = 1.04000002384162 
k2 = .940000023841624 
k3 = .950000023841624 
k4 = .730000023841624 
k5 = .880000023841624 
k6 = .86000002384162