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Production prediction method and system based on nonlinear regression model parameters

A technology of nonlinear regression and model parameters, applied in the field of scientific computing, can solve problems such as high dependence, wrong prediction, and iteration failure

Inactive Publication Date: 2015-12-30
SOUTH CHINA NORMAL UNIVERSITY
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

[0003] But in fact, as far as the existing production forecasting methods based on nonlinear regression model parameters (mainly the Gauss-Newton method), the determination of nonlinear regression parameters often takes a lot of time and is inefficient
At the same time, the iterative method has a more fatal problem: the dependence on the initial estimated value of the parameter is very high: a slight deviation between the estimated value and the true value will cause the iteration to fail
However, these problems of the existing production forecasting method based on nonlinear regression model parameters directly lead to the company or factory's prediction lag or wrong prediction of future data, causing the company or factory to suffer losses.

Method used

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  • Production prediction method and system based on nonlinear regression model parameters
  • Production prediction method and system based on nonlinear regression model parameters

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0051] The relationship between the length (Y) and age (X) of manatee dugongs using asymptotic regression model Y=α-βγ X express. The following is the initial data (units omitted):

[0052] X 1.0 1.5 1.5 1.5 2.5 4.0 5.0 5.0 7.0 Y 1.80 1.85 1.87 1.77 2.02 2.27 2.15 2.26 2.35 X 8.0 8.5 9.0 9.5 9.5 10.0 12.0 12.0 13.0 Y 2.47 2.19 2.26 2.40 2.39 2.41 2.50 2.32 2.43 X 13.0 14.5 15.5 15.5 16.5 17.0 22.5 29.0 31.5 Y 2.47 2.56 2.56 2.47 2.64 2.56 2.70 2.72 2.57

[0053] First use the Gauss-Newton method to calculate, give a rough initial value, use the new method of the present invention to do 5 times in a row, then give the accurate initial value, and then use the new method to do 5 times in a row, and the output results obtained are organized into Table 1:

[0054] Table 1. Comparison of results

[0055]

[0056] The error refers to the sum of the absolute value of the dispersion between the...

Embodiment 2

[0059] The relationship between wheat yield (Y) and fertilizer level (X) using asymptotic regression model Y=α-βγ X express. The following is the initial data (units omitted):

[0060] X 0 10 20 30 40 Y 26.2 30.4 36.3 37.8 38.6

[0061] The method is similar to the above example, and Table 2 is obtained.

[0062] Table 2. Results comparison

[0063]

[0064] It can be seen from the above table 2 that the new method of the present invention has highlighted its advantages no matter what the initial value state is. In the state of rough initial value, the time consumption is much less than that of the Gauss-Newton method, and even the accuracy exceeds the original method. The results are shown in Figure 2(a). And when the initial value state is very good, the production prediction method based on the nonlinear regression model parameters of the present invention completely surpasses the production prediction method using the Gauss-Newton method, ...

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Abstract

The invention discloses a production prediction method based on nonlinear regression model parameters. The production prediction method comprises the following steps: S1. obtaining an initial interval of the parameters; S2. carrying out equipartition processing on the initial interval to obtain all small intervals; S3. randomly taking points in each small interval; S4. finding the most suitable point combination according to given standards; S5. according to the found point combination, finding a new initial interval of the parameters; and S6. repeating steps S1 to S5, until the accuracy reaches a predetermined value. The method disclosed by the invention is high in calculation speed, and not high in sensitivity to initial values.

Description

technical field [0001] The invention relates to the field of scientific computing, in particular to a production prediction method and system based on nonlinear regression model parameters. Background technique [0002] Nonlinear problems are often encountered in real life: for example, in company operations, factory production, etc., various nonlinear problems will be encountered. In order to make decisions, describing the law of nonlinear data and predicting nonlinear data has become an important link in production and management. Nonlinear regression is an important tool for analyzing nonlinear data. Since nonlinear models are generally more complex, it is not easy to obtain estimates of their parameters. Therefore, obtaining the results of nonlinear regression in a timely manner can often bring huge benefits to production and management. [0003] But in fact, as far as the existing production forecasting methods based on nonlinear regression model parameters (mainly th...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50
Inventor 王毅刚谢炜琛
Owner SOUTH CHINA NORMAL UNIVERSITY
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