CN109635344A - Effectiveness Evaluation Model preparation method and device based on l-G simulation test - Google Patents
Effectiveness Evaluation Model preparation method and device based on l-G simulation test Download PDFInfo
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Abstract
The present invention provides a kind of Effectiveness Evaluation Model preparation method based on l-G simulation test, which includes: acquisition test data；The relational expression for each index parameter that efficiency index and test obtain in each test of definition are as follows: y=β0+β1f(x1)+β2f(x2)+…+βif(xi)+ε；Significance analysis is carried out to index parameter, rejects inapparent index parameter；Establish βif(xi) Conventional mathematical model, and using regression algorithm to the βif(xi) Conventional mathematical model analyzed, and determine βiWith f (xi) end value；To βiWith f (xi) the hypothetical inspection that is returned of end value；If obtaining Effectiveness Evaluation Model by examining.The present invention is handled data by stochastic finite element principle, is efficiently avoided existing traditional means and is lacked verification experimental verification and the analysis verification method based on passing experience, has certain guidance meaning to real aircraft design.
Description
Technical field
The invention belongs to aircraft technology fields, specifically provide a kind of Effectiveness Evaluation Model preparation method based on l-G simulation test
And device.
Background technique
Currently, existing aircraft efficiency estimation method more be confined to expert graded, ADC method, Lanchester equation method,
SEA method etc..From principle, expert graded depends on the level and subjective judgement of marking person, more depends on personal experience；
ADC method and Lanchester equation method depend on existing efficiency index to carry out availability and degree of belief derivation, to practical Campaign Process
Evaluation reference meaning is small.SEA method is more suitable for the cluster fight assessment of extensive more arm of the services.Above method is carried out to fighter plane
When measures of effectiveness, it cannot be guaranteed that the research for agreeing with evaluation requirement completely, and having carried out is mostly theory deduction, lack mass efficient
Data are as verifying support.
Thus, it is desirable to have a kind of technical solution overcomes or at least mitigates at least one above problem of the prior art.
Summary of the invention
The Effectiveness Evaluation Model preparation method and device that the purpose of the present invention is to provide a kind of based on l-G simulation test come gram
Clothes at least mitigate at least one above problem in the prior art.
To achieve the above object, the present invention provides a kind of Effectiveness Evaluation Model preparation method based on l-G simulation test, institute
Stating preparation method includes: acquisition test data, wherein the test data includes efficiency index and index parameter；Definition is each
The relational expression for each index parameter that efficiency index described in test and test obtain are as follows: y=β0+β1f(x1)+β2f(x2)+…
+βif(xi)+ε, wherein y is the metric for characterizing efficiency index, and x is the experimental data defined by evaluation requirement, βiFor wait estimate ginseng
Number, ε are the random quantity of mutually independent Normal Distribution；Significance analysis is carried out to the index parameter, is rejected not significant
Index parameter；Establish βif(xi) Conventional mathematical model, and using regression algorithm to the βif(xi) Conventional mathematical model
It is analyzed, and determines βiWith f (xi) end value；To the βiWith f (xi) the hypothetical inspection that is returned of end value；
If obtaining Effectiveness Evaluation Model by examining.
Preferably, the Conventional mathematical model includes: linear function model, exponential Function Model, logarithmic function model, bears
Exponential Function Model, hyperbolic function model, power exponential function model, Logistic curvilinear function model and other according to work
Journey experience and the function model that property characteristic is established.
Preferably, the β is examinediWith f (xi) the methods of validity of end value regression forms include: to utilize statistics
The method of middle hypothesis testing carries out hypothetical inspection, determines the βiWith f (xi) end value regression forms validity；Or
Person verifies the β using test result more under the same termsiWith f (xi) end value regression forms it is effective
Property.
Preferably, in the statistics hypothesis testing method, comprising: propose two hypothesis H0、H1, wherein H0It is former false
If H1For alternative hypothesis；Statistic is constructed, the value of statistic is divided into disjoint two parts；When the statistic of sample is seen
When examining value and falling into region of rejection, refuse H0；When the statistic observed value of sample, which is fallen into, receives domain, receive H0。
Preferably, the regression algorithm include: quasi- Newton method, simple face body climbing method, Evolve-ment law of checking the mark, maximum inheritance act,
Genetic algorithm and simulated annealing.
Preferably, the method also includes: if not by examine, recalculate βiWith f (xi) end value；To described
βiWith f (xi) the hypothetical inspection that is returned of end value, examined until passing through.
On the other hand, the present invention also provides a kind of, and the Effectiveness Evaluation Model based on l-G simulation test obtains device, described
Obtaining device includes: that test data obtains module, for obtaining test data, wherein the test data includes efficiency index
And index parameter；Definition module, for will efficiency index described in test and test obtain every time each index parameter
Relational expression is defined as: y=β0+β1f(x1)+β2f(x2)+…+βif(xi)+ε, wherein y is efficiency index, and x is index parameter, βi
For parameter to be estimated, ε is the random quantity of mutually independent Normal Distribution；Module is rejected in analysis, for the index parameter
Significance analysis is carried out, inapparent index parameter is rejected；Determining module is analyzed, for establishing βif(xi) Conventional mathematical mould
Type, and using regression algorithm to the βif(xi) Conventional mathematical model analyzed, and determine βiWith f (xi) end value；
First inspection module, for the βiWith f (xi) the hypothetical inspection that is returned of end value；Assessment obtains module, is used for
When passing through inspection, Effectiveness Evaluation Model is obtained.
Preferably, the acquisition device further include: inspection module is calculated, for recalculating β when not passing through inspectioni
With f (xi) end value, and to βiWith f (xi) the hypothetical inspection that is returned of end value.
Preferably, the Conventional mathematical type includes: linear function model, exponential Function Model, logarithmic function model, negative finger
Number function models, hyperbolic function model, power exponential function model, Logistic curvilinear function model and other according to engineering
Experience and the function model that property characteristic is established.
Preferably, first inspection module includes: proposition unit, for proposing two hypothesis H0、H1, wherein H0For original
It is assumed that H1For alternative hypothesis；The value of statistic is divided into disjoint two parts for constructing statistic by structural unit；It refuses
Exhausted unit, for refusing H when the statistic observed value of sample falls into region of rejection0；Receiving unit, for the statistics in sample
When amount observed value falls into reception domain, receive H0。
It will be appreciated to those of skill in the art that being united in the preferred technical solution of the present invention by probability and mathematics
Meter principle handles data, efficiently avoids existing traditional means and lacks verification experimental verification and the analysis based on passing experience
Verification method.Moreover, each submodel βif(xi) it can be used to characterize some aspect in the efficiency of aircraft, it is mapped by parameter
Method adjust the corresponding design objective of aircraft to meet performance requirements, there is certain guidance meaning to real aircraft design.
Detailed description of the invention
Fig. 1 is the flow diagram of preparation method provided in an embodiment of the present invention；
Fig. 2 is the schematic diagram that the residual sum of squares (RSS) of the metric of characterization efficiency index provided in an embodiment of the present invention is 3.5；
Fig. 3 is the signal that the residual sum of squares (RSS) of the metric of characterization efficiency index provided in an embodiment of the present invention is 0.24
Figure；
Fig. 4 is the schematic diagram that the residual sum of squares (RSS) of the metric of characterization efficiency index provided in an embodiment of the present invention is 0.
Specific embodiment
The preferred embodiment of the present invention described with reference to the accompanying drawings.It will be apparent to a skilled person that this
A little embodiments are used only for explaining technical principle of the invention, it is not intended that limit the scope of the invention.
Referring to Fig. 1, the Effectiveness Evaluation Model preparation method based on l-G simulation test specifically includes the following steps:
S101 obtains test data.
Wherein, test data includes efficiency index and index parameter, for example, the battlefield data of aircraft, weapon, equipment.
The acquisition of test data can be according to specific tactical problem, carries out people and obtains in the real-time no-data region of ring
Above-mentioned data.
S102 defines the relational expression for each index parameter that efficiency index and test obtain in test every time are as follows: y=β0+β1f
(x1)+β2f(x2)+…βif(xi)+ε。
Wherein, y is the metric for characterizing efficiency index, and x is the test data defined by evaluation requirement, βiFor wait estimate ginseng
Number, ε are the random quantity of mutually independent Normal Distribution.
The metric of efficiency index, which can be, gives a mark test result according to the casualty situations of red blue party in experiment, and score can
To be denoted as 0-12 points of integer value, 0-5 points are lost for red, and 6 points are draw, and 7-12 points is won for red.
It can be detective distance, maximum lasting capture time, first enemy's transmitting for the first time by the test data that evaluation requirement defines
Distance.
S103 carries out significance analysis to index parameter, rejects inapparent index parameter；
S104 establishes βif(xi) Conventional mathematical model, and using regression algorithm to βif(xi) Conventional mathematical model into
Row analysis, and determine βiWith f (xi) end value.
Wherein, conventional mathematical model may include following mathematical model:
Linear function model: y=ax+b；
Exponential Function Model:
Logarithmic function model: y=a+b ln (x+c)；
Hyperbolic function model:
Power exponential function model: y=a (x+b)c；
Logistic curvilinear function model:
In above formula, x, y are model variable, and a, b, c is model parameter.
Conventional mathematical model can also be the function model established according to engineering experience and plant characteristic.
In the present embodiment, regression algorithm may be selected quasi- Newton method, simple face body climbing method, Evolve-ment law of checking the mark, maximum inheritance act,
Genetic algorithm, simulated annealing etc..
By utilizing one of above-mentioned regression algorithm or a variety of couples of βif(xi) various Conventional mathematical models divided
Analysis determines that the available index of regression model is minimum for y value residual sum of squares (RSS) after fitting.
S105, to βiWith f (xi) the hypothetical inspection that is returned of end value.
Wherein, the method for examining the validity of regression forms to can use hypothesis testing in statistics carries out hypothetical inspection
It tests, determines the βiWith f (xi) end value regression forms validity；Also it can use under the same terms and more frequently try
Result is tested to verify the βiWith f (xi) end value regression forms validity.
β obtained in step s104if(xi) cannot be determined to reflect the relationship of true efficiency and parameter, it needs to examine
The validity of estimated regression forms.Specifically, it can be tested using following methods:
Carry out hypothetical inspection using the method for hypothesis testing in statistics, determines the effective of estimated regression forms
Property.
Two hypothesis H are proposed first0、H1, claim H0For null hypothesis, H1For alternative hypothesis；Then statistic is constructed, statistics
The value of amount is divided into the two parts for being not desired to hand over, and refuses when the statistic observed value of sample falls into a portion (referred to as region of rejection)
Exhausted H0, receive H when falling into another part (referred to as acceptance region)0。
S106, if obtaining Effectiveness Evaluation Model by examining.
S107, if not recalculating β by examiningiWith f (xi) end value, and to the βiWith f (xi) it is final
The hypothetical inspection that value is returned is examined until passing through.
The method of inspection is identical as the method in above-mentioned steps, and details are not described herein.
In the present embodiment, it returns and calculates and can be calculated using business software 1stOpt15PRO, to select maximum
It for inheritance act calculates, is calculated by successive ignition, changes the calculation method for calculating and disposing gradually correction model and initial value, obtain
Regression result is as shown in Figure 2, Figure 3 and Figure 4, and the point in figure is test result, and line is fitting result.
From Fig. 2 and 3 and Fig. 4, it can be seen that the residual sum of squares (RSS) of the metric of the characterization efficiency index after fitting is successively
Reduce, data fit statistics rule can obtain Effectiveness Evaluation Model (the i.e. residuals squares of the metric of characterization efficiency index
With for 0).
On the other hand, a kind of Effectiveness Evaluation Model based on l-G simulation test of the present invention obtains device, the acquisition device packet
It includes:
Test data obtains module, for obtaining test data, wherein the test data includes efficiency index and index
Parameter.
Definition module, the relationship of each index parameter for obtaining efficiency index described in each test and test
Formula is defined as: y=β0+β1f(x1)+β2f(x2)+…+βif(xi)+ε, wherein y is efficiency index, and x is index parameter, βiFor to
Estimate parameter, ε is the random quantity of mutually independent Normal Distribution.
Module is rejected in analysis, for carrying out significance analysis to the index parameter, rejects inapparent index parameter.
Determining module is analyzed, for establishing βif(xi) Conventional mathematical model, and using regression algorithm to the βif(xi)
Conventional mathematical model analyzed, and determine βiWith f (xi) end value.
First inspection module, for the βiWith f (xi) the hypothetical inspection that is returned of end value.
Assessment obtains module, for obtaining Effectiveness Evaluation Model when passing through inspection.
Inspection module is calculated, for recalculating β when not passing through inspectioniWith f (xi) end value, and to βiWith f
(xi) the hypothetical inspection that is returned of end value.
In an embodiment of the invention, the Conventional mathematical type include: linear function model, it is exponential Function Model, right
Number function model, negative exponential function model, hyperbolic function model, power exponential function model, Logistic curvilinear function model
And other according to engineering experience with to property characteristic establish function model.
The expression of mathematical model has been given in the methods described above, is not repeating herein.
In an embodiment of the invention, the first inspection module includes:
Unit is proposed, for proposing two hypothesis H0、H1, wherein H0For null hypothesis, H1For alternative hypothesis 0
The value of statistic is divided into disjoint two parts 0 for constructing statistic by structural unit
Refuse unit, for refusing H when the statistic observed value of sample falls into region of rejection00
Receiving unit receives H when falling into reception domain for the statistic observed value in sample0。
So far, it has been combined preferred embodiment shown in the drawings and describes technical solution of the present invention, still, this field
Technical staff is it is easily understood that protection scope of the present invention is expressly not limited to these specific embodiments.Without departing from this
Under the premise of the principle of invention, those skilled in the art can make equivalent change or replacement to the relevant technologies feature, these
Technical solution after change or replacement will fall within the scope of protection of the present invention.
Claims (10)
1. a kind of Effectiveness Evaluation Model preparation method based on l-G simulation test, which is characterized in that the preparation method includes
Obtain test data, wherein the test data includes efficiency index and index parameter；
The relational expression of definition each index parameter that efficiency index described in test and test obtain every time are as follows:
Y=β0+β1f(x1)+β2f(x2)+…+βif(xi)+ε,
Wherein, y is the metric for characterizing efficiency index, and x is the experimental data defined by evaluation requirement, βiFor parameter to be estimated, ε is
The random quantity of mutually independent Normal Distribution；
Significance analysis is carried out to the index parameter, rejects inapparent index parameter；
Establish βif(xi) Conventional mathematical model, and using regression algorithm to the βif(xi) Conventional mathematical model divided
Analysis, and determine βiWith f (xi) end value；
To the βiWith f (xi) the hypothetical inspection that is returned of end value；
If obtaining Effectiveness Evaluation Model by examining.
2. the Effectiveness Evaluation Model preparation method according to claim 1 based on l-G simulation test, which is characterized in that described normal
Advising mathematical model includes
Linear function model, exponential Function Model, logarithmic function model, negative exponential function model, hyperbolic function model, power refer to
Number function models, Logistic curvilinear function model and other according to engineering experience with to property characteristic establish function model.
3. the Effectiveness Evaluation Model preparation method according to claim 1 based on l-G simulation test, which is characterized in that inspection institute
State βiWith f (xi) end value regression forms validity method carry out including the use of the method for hypothesis testing in statistics it is false
If property is examined, the β is determinediWith f (xi) end value regression forms validity；Alternatively,
The β is verified using test result more under the same termsiWith f (xi) end value regression forms it is effective
Property.
4. the Effectiveness Evaluation Model preparation method according to claim 3 based on l-G simulation test, which is characterized in that the system
The method of hypothetical inspection in meter, including
It is proposed two hypothesis H0、H1, wherein H0For null hypothesis, H1For alternative hypothesis；
Statistic is constructed, the value of statistic is divided into disjoint two parts；
When the statistic observed value of sample falls into region of rejection, refuse H0；
When the statistic observed value of sample, which is fallen into, receives domain, receive H0。
5. the Effectiveness Evaluation Model preparation method according to claim 1 based on l-G simulation test, which is characterized in that described time
Reduction method includes
Quasi- Newton method, simple face body climbing method, Evolve-ment law of checking the mark, maximum inheritance act, genetic algorithm and simulated annealing.
6. the Effectiveness Evaluation Model preparation method according to any one of claim 1 to 5 based on l-G simulation test, feature
It is, the method also includes
If not recalculating β by examiningiWith f (xi) end value；
To the βiWith f (xi) the hypothetical inspection that is returned of end value, examined until passing through.
7. a kind of Effectiveness Evaluation Model based on l-G simulation test obtains device, which is characterized in that the acquisition device includes
Test data obtains module, for obtaining test data, wherein the test data includes efficiency index and index ginseng
Number；
The relational expression of definition module, each index parameter for obtaining efficiency index described in each test with test is determined
Justice are as follows:
Y=β0+β1f(x1)+β2f(x2)+…+βif(xi)+ε,
Wherein, y is efficiency index, and x is index parameter, βiFor parameter to be estimated, ε is the random of mutually independent Normal Distribution
Amount；
Module is rejected in analysis, for carrying out significance analysis to the index parameter, rejects inapparent index parameter；
Determining module is analyzed, for establishing βif(xi) Conventional mathematical model, and using regression algorithm to the βif(xi) it is normal
Rule mathematical model is analyzed, and determines βiWith f (xi) end value；
First inspection module, for the βiWith f (xi) the hypothetical inspection that is returned of end value；
Assessment obtains module, for obtaining Effectiveness Evaluation Model when passing through inspection.
8. the Effectiveness Evaluation Model according to claim 7 based on l-G simulation test obtains device, which is characterized in that described to obtain
Obtaining device further includes
Inspection module is calculated, for recalculating β when not passing through inspectioniWith f (xi) end value, and to βiWith f (xi) most
The hypothetical inspection that final value is returned.
9. the Effectiveness Evaluation Model according to claim 7 based on l-G simulation test obtains device, which is characterized in that described normal
Advising mathematics type includes
Linear function model, exponential Function Model, logarithmic function model, negative exponential function model, hyperbolic function model, power refer to
Number function models, Logistic curvilinear function model and other according to engineering experience with to property characteristic establish function model.
10. the Effectiveness Evaluation Model according to claim 7 based on l-G simulation test obtains device, which is characterized in that described
First inspection module includes
Unit is proposed, for proposing two hypothesis H0、H1, wherein H0For null hypothesis, H1For alternative hypothesis；
The value of statistic is divided into disjoint two parts for constructing statistic by structural unit；
Refuse unit, for refusing H when the statistic observed value of sample falls into region of rejection0；
Receiving unit receives H when falling into reception domain for the statistic observed value in sample0。
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| CN110544011A (en) * | 2019-07-31 | 2019-12-06 | 北京航空航天大学 | Intelligent system combat effectiveness evaluation and optimization method |
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| CN112711739A (en) * | 2019-10-25 | 2021-04-27 | 腾讯科技（深圳）有限公司 | Data processing method and device, server and storage medium |
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| CN115906523A (en) * | 2022-12-28 | 2023-04-04 | 北京航天飞行控制中心 | Method and device for optimizing parameters to be estimated for track calculation |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110544011A (en) * | 2019-07-31 | 2019-12-06 | 北京航空航天大学 | Intelligent system combat effectiveness evaluation and optimization method |
| CN110544011B (en) * | 2019-07-31 | 2023-03-24 | 北京航空航天大学 | Intelligent system combat effectiveness evaluation and optimization method |
| CN112711739A (en) * | 2019-10-25 | 2021-04-27 | 腾讯科技（深圳）有限公司 | Data processing method and device, server and storage medium |
| CN112711739B (en) * | 2019-10-25 | 2024-05-28 | 腾讯科技（深圳）有限公司 | Data processing method and device, server and storage medium |
| CN111783308A (en) * | 2020-07-08 | 2020-10-16 | 西南交通大学 | Method for accurately predicting tunnel surrounding rock displacement |
| CN112146520A (en) * | 2020-08-11 | 2020-12-29 | 南京理工大学 | Method and system for calculating hearing threshold transfer of sound wave weapon after being hit |
| CN115544772A (en) * | 2022-10-12 | 2022-12-30 | 湖北文理学院 | Method, device and terminal for multivariate regression and fitting of dynamic parachute opening simulation data of lifesaving parachute |
| CN115544772B (en) * | 2022-10-12 | 2024-04-02 | 湖北文理学院 | Dynamic parachute opening simulation data multiple regression and fitting method, device and terminal for parachute |
| CN115906523A (en) * | 2022-12-28 | 2023-04-04 | 北京航天飞行控制中心 | Method and device for optimizing parameters to be estimated for track calculation |
| CN115906523B (en) * | 2022-12-28 | 2024-04-30 | 北京航天飞行控制中心 | Parameter optimization method and device for track calculation to be estimated |
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