Alex Sha
20 of your answers have been accepted
Awarded to Alex Sha on 30 Oct 2021
Contribute at least one answer each month for 12 consecutive months
Awarded to Alex Sha on 03 Apr 2020
Answer 10 questions that were unanswered for more than 30 days
Awarded to Alex Sha on 09 Jan 2020
Provide your first answer ever to someone else's question
Awarded to Alex Sha on 22 Apr 2019
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已回答
Fitting a model to my data using non linear least square fit method
The best solution: Root of Mean Square Error (RMSE): 0.0143800358786989 Correlation Coef. (R): 0.998885623657614 R-Square: 0....
Fitting a model to my data using non linear least square fit method
The best solution: Root of Mean Square Error (RMSE): 0.0143800358786989 Correlation Coef. (R): 0.998885623657614 R-Square: 0....
4 days 前 | 0
已回答
Fsolve don't work good with trigonometric
There are some solutions like below No. 1 2 3 4 5 xa1 0.835289443057692 1.97769580537186 0.119396464246135 0.813305799520437 0...
Fsolve don't work good with trigonometric
There are some solutions like below No. 1 2 3 4 5 xa1 0.835289443057692 1.97769580537186 0.119396464246135 0.813305799520437 0...
25 days 前 | 0
已回答
fsolve result is not desirable even giving a close starting point
For Qingbin's equations, although it is a problem that has passed a long time, it is worth and interesting to have a try, there ...
fsolve result is not desirable even giving a close starting point
For Qingbin's equations, although it is a problem that has passed a long time, it is worth and interesting to have a try, there ...
28 days 前 | 0
已回答
using lsqnonlin with multiple functions
@joshua payne refer to the results below Sum Squared Error (SSE): 0.377485784540046 Root of Mean Square Error (RMSE): 0.082102...
using lsqnonlin with multiple functions
@joshua payne refer to the results below Sum Squared Error (SSE): 0.377485784540046 Root of Mean Square Error (RMSE): 0.082102...
2 months 前 | 0
已回答
Least squares linear regression with constraints
If using direct nonlinear fitting a1 59.737732722511 a2 2.72067588034148 a3 -0.192039313150924 Whi...
Least squares linear regression with constraints
If using direct nonlinear fitting a1 59.737732722511 a2 2.72067588034148 a3 -0.192039313150924 Whi...
2 months 前 | 0
已回答
How do I curve fit the data set
@Prajwal Magadi, one more function: Sum Squared Error (SSE): 75571.6557870726 Root of Mean Square Error (RMSE): 2.7490299341...
How do I curve fit the data set
@Prajwal Magadi, one more function: Sum Squared Error (SSE): 75571.6557870726 Root of Mean Square Error (RMSE): 2.7490299341...
2 months 前 | 1
已回答
fitting data with a combination of exponential and linear form ( a*exp(-x/b)+c*x+d )
If taking fitting function as "y=a*exp(-x/b)+c*x+d", the result will be: Sum Squared Error (SSE): 0.473516174967249 Root of Me...
fitting data with a combination of exponential and linear form ( a*exp(-x/b)+c*x+d )
If taking fitting function as "y=a*exp(-x/b)+c*x+d", the result will be: Sum Squared Error (SSE): 0.473516174967249 Root of Me...
3 months 前 | 1
已回答
Fitting multiple exponential function .
@Saroj Poudyal, the result you obtained is not the best one, refer to the global optimization solution below: Sum Squared Error...
Fitting multiple exponential function .
@Saroj Poudyal, the result you obtained is not the best one, refer to the global optimization solution below: Sum Squared Error...
4 months 前 | 1
| 已接受
已回答
How to fit multiple gaussian in a curve ?
For the summation of 6 Gaussians function: Sum Squared Error (SSE): 2.61218021364296E-9 Root of Mean Square Error (RMSE): 4.49...
How to fit multiple gaussian in a curve ?
For the summation of 6 Gaussians function: Sum Squared Error (SSE): 2.61218021364296E-9 Root of Mean Square Error (RMSE): 4.49...
6 months 前 | 0
已回答
Curve fitting the data series
Refer to the results below, should be the unique global solution: Sum Squared Error (SSE): 0.0378758633912789 Root of Mean Squ...
Curve fitting the data series
Refer to the results below, should be the unique global solution: Sum Squared Error (SSE): 0.0378758633912789 Root of Mean Squ...
6 months 前 | 1
已回答
Genetic Algorithm not returning best found solution
Taking my experience, GA is not an efficient and ideal global optimization algorithm, in lots of cases, GA like random reserach ...
Genetic Algorithm not returning best found solution
Taking my experience, GA is not an efficient and ideal global optimization algorithm, in lots of cases, GA like random reserach ...
10 months 前 | 0
已回答
Solving a system of Non-linear Equations with Complex numbers
There are much more solutions else: x1: 5000+3401.68025708298i x2: 5000-3401.68025708301i x3: -3.62536433474507+0i x1: 5...
Solving a system of Non-linear Equations with Complex numbers
There are much more solutions else: x1: 5000+3401.68025708298i x2: 5000-3401.68025708301i x3: -3.62536433474507+0i x1: 5...
11 months 前 | 0
已回答
How do I fit a regression equation to find coefficients and exponents?
Although the results may seem strange, mathematically speaking, the result below is the best one: Sum Squared Error (SSE): 87...
How do I fit a regression equation to find coefficients and exponents?
Although the results may seem strange, mathematically speaking, the result below is the best one: Sum Squared Error (SSE): 87...
11 months 前 | 0
已回答
How to constraint the values of fitted parameters with lsqcurvefit?
hi, the result is good enough Sum Squared Error (SSE): 0.0105245967805521 Root of Mean Square Error (RMSE): 0.01938758511131 ...
How to constraint the values of fitted parameters with lsqcurvefit?
hi, the result is good enough Sum Squared Error (SSE): 0.0105245967805521 Root of Mean Square Error (RMSE): 0.01938758511131 ...
1 year 前 | 1
| 已接受
已回答
curve fitting tool custom equation
if taking only part of data, for example, from No. 105 to No. 300, then the result will looks good Sum Squared Error (SSE): 111...
curve fitting tool custom equation
if taking only part of data, for example, from No. 105 to No. 300, then the result will looks good Sum Squared Error (SSE): 111...
1 year 前 | 0
已回答
The fsolve function fails to give me an answer for seven unknowns. What should I do?
Just doing some equivalent deformation (change division into multiplication), for example, form: f(5)=((x(1)^3)*(x(2))/(x(4)*...
The fsolve function fails to give me an answer for seven unknowns. What should I do?
Just doing some equivalent deformation (change division into multiplication), for example, form: f(5)=((x(1)^3)*(x(2))/(x(4)*...
1 year 前 | 0
已回答
How to curve fit an equation that gets values from another equation?
How about the results below: Residual Sum of Squares (RSS): 34.9122402573179 Root of Mean Square Error (RMSE): 0.9585109100402...
How to curve fit an equation that gets values from another equation?
How about the results below: Residual Sum of Squares (RSS): 34.9122402573179 Root of Mean Square Error (RMSE): 0.9585109100402...
1 year 前 | 0
已回答
Curve fitting with custom function
It is hard to get stable and unique result for Da125's problem, especially for parameters of "lambda" and "n". refer to the resu...
Curve fitting with custom function
It is hard to get stable and unique result for Da125's problem, especially for parameters of "lambda" and "n". refer to the resu...
2 years 前 | 0
已回答
FItting 3d with fourier
If taking the fitting function as: the result will be: Root of Mean Square Error (RMSE): 0.000481289122874164 Sum of Square...
FItting 3d with fourier
If taking the fitting function as: the result will be: Root of Mean Square Error (RMSE): 0.000481289122874164 Sum of Square...
2 years 前 | 2
| 已接受
已回答
PARAMETER estimation of kinetic and adsorbtion constant of langmuir hinshelwood haugen watson model
if want all parameters to be positive: Root of Mean Square Error (RMSE): 0.0035717712315911 Sum of Squared Residual: 0.0003572...
PARAMETER estimation of kinetic and adsorbtion constant of langmuir hinshelwood haugen watson model
if want all parameters to be positive: Root of Mean Square Error (RMSE): 0.0035717712315911 Sum of Squared Residual: 0.0003572...
2 years 前 | 0
已回答
How to obtain model parameters by fitting experimental data to the monod model. monod model.
Refer to the results below: Root of Mean Square Error (RMSE): 1.50437634361661 Sum of Squared Residual: 81.4733345963976 Corr...
How to obtain model parameters by fitting experimental data to the monod model. monod model.
Refer to the results below: Root of Mean Square Error (RMSE): 1.50437634361661 Sum of Squared Residual: 81.4733345963976 Corr...
2 years 前 | 0
| 已接受
已回答
curve fitting a custom equation
Hi, all, as mentioned by Walter Roberson, the resule I provided above is obtained by using 1stOpt, the biggest advantage, for 1s...
curve fitting a custom equation
Hi, all, as mentioned by Walter Roberson, the resule I provided above is obtained by using 1stOpt, the biggest advantage, for 1s...
2 years 前 | 0
| 已接受
已回答
I want to solve 2 equations with 2 variables but it cant be solved and the command window shows fsolve stopped because the last step was ineffective
Hi, the result below should be one solution x1: -52.5485799573576 x2: 12.1032014174617
I want to solve 2 equations with 2 variables but it cant be solved and the command window shows fsolve stopped because the last step was ineffective
Hi, the result below should be one solution x1: -52.5485799573576 x2: 12.1032014174617
2 years 前 | 0
已回答
How to solve a system of second order nonlinear differential equations with boundary conditions
Hi, the chart will be given automatically in 1stOpt UI, also you can make chart yourself by using the result data given above. 1...
How to solve a system of second order nonlinear differential equations with boundary conditions
Hi, the chart will be given automatically in 1stOpt UI, also you can make chart yourself by using the result data given above. 1...
2 years 前 | 0
已回答
I need to fits the attached data as in image
Hi, the fitting function "y = p1/(1+Exp(p2+p3*x)+p4*x)^p5" is pretty good for all data set, where y=L, x=T 1: T1-L1: Root of ...
I need to fits the attached data as in image
Hi, the fitting function "y = p1/(1+Exp(p2+p3*x)+p4*x)^p5" is pretty good for all data set, where y=L, x=T 1: T1-L1: Root of ...
2 years 前 | 2
| 已接受
已回答
Objective function is returning undefined values at initial point. lsqcurvefit cannot continue.
The best results are as below, hard to be obtained since the initial-start values are impossible to be guessed reasonally. Root...
Objective function is returning undefined values at initial point. lsqcurvefit cannot continue.
The best results are as below, hard to be obtained since the initial-start values are impossible to be guessed reasonally. Root...
2 years 前 | 1
| 已接受
已回答
Kinetic parameter estimation and initial time setting
Hi, see the results below: Root of Mean Square Error (RMSE): 4.00513564326846 Sum of Squared Residual: 320.822230419589 Corre...
Kinetic parameter estimation and initial time setting
Hi, see the results below: Root of Mean Square Error (RMSE): 4.00513564326846 Sum of Squared Residual: 320.822230419589 Corre...
2 years 前 | 0
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