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Alex Sha


Last seen: Today |  自 2019 起处于活动状态

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已回答
Solution of system of nonlinear equations
@Swami Between the range [0,1] you specified, most likely, there should be no real numbers solution，an approximate solution is: ...

1 month 前 | 0

已回答
Why is Curve fitting tool not giving good results even with the R-square value of 0.99?
@Muhammad Arsal the goodness of a fitting should be judged by SSE (Sum of Squared Error), but not R-Square value, the former rep...

1 month 前 | 0

已回答
Non-linear Algebraic 36 equations unsloved
@MINATI PATRA please giving out the code you used, or the detail description of your 36 equations

6 months 前 | 0

已回答
problem in curve fitting using summation of sine functions
@nihal if don't mind fitting function other than summation of sine, much better result will be achieved. For phi_dot data: R...

9 months 前 | 0

| 已接受

已回答
Exp Fit Error: Error using fit>iFit (line 340) NaN computed by model function, fitting cannot continue. Try using or tightening upper and lower bounds on coefficients.
There are two solutions: 1: Sum Squared Error (SSE): 0.0371625579290083 Root of Mean Square Error (RMSE): 0.0609611006536204 ...

9 months 前 | 0

已回答
Possibly spurious solutions - Matlab blocked with no answers
There is a approximate solution for original equations： p2: 35001350.3279785 p3: 35113789.3799513 u2: -8.90075607998193 u3: ...

9 months 前 | 0

已回答
Interpolation schemes that produce positive second derivatives of the interpolant
How about to replace interpolation with a fitting function, which ensure non-negative second derivatives: 1: For data: x = [...

10 months 前 | 0

已回答
How to find the equation of the data available of a graph?
Try the fitting function below: Sum Squared Error (SSE): 1.05153845767184 Root of Mean Square Error (RMSE): 0.17850714760936...

10 months 前 | 0

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已回答
can help me to found empirical equation for data L1 vs T1
How about the one below, much simple and works well: Sum Squared Error (SSE): 0.348116593172766 Root of Mean Square Error (R...

11 months 前 | 2

| 已接受

已回答
can help me to found empirical equation for data L1 vs T1
It is not easy to find a function to describe the curve. Refer to the fitting function and result below: Sum Squared Error (S...

11 months 前 | 1

已回答
how to fit the coupled differential equations and get the coefficients?
Refer to the results below: Sum Squared Error (SSE): 46715596564.0427 Root of Mean Square Error (RMSE): 35062.1990798768 Corr...

1 year 前 | 0

已回答
Derivative not working plot
Unfortunately, the "exp2" fitting result below given by Matlab is not correctly. theFit = General model Exp2: theF...

1 year 前 | 0

已回答
curve fitting exponential function with two terms
If taking the fitting function as: y=a*exp(b*x) + c*exp(d*x); and also taking the data like below directly; x = [6500 6350 580...

1 year 前 | 0

已回答
Damped Oscillation Equation Fitting
if taking fitting function as: x=a*exp(-b*t)*sin(w*t+phi) for trial-1 Sum Squared Error (SSE): 9.1948847955911E-5 Root of Mea...

1 year 前 | 0

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....

1 year 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...

1 year 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 ...

1 year 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...

1 year 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...

1 year 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...

1 year 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...

1 year 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...

1 year 前 | 1

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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...

1 year 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...

1 year 前 | 1

已回答
Non-linear Multi-Variable Fitting
If possible, post out your data file please

2 years 前 | 0

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 ...

2 years 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...

2 years 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...

2 years 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 ...

2 years 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...

2 years 前 | 0

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