CN108805358A - A kind of Gray Dynamic reinforcing prediction technique based on multiple factors - Google Patents
A kind of Gray Dynamic reinforcing prediction technique based on multiple factors Download PDFInfo
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- CN108805358A CN108805358A CN201810614457.XA CN201810614457A CN108805358A CN 108805358 A CN108805358 A CN 108805358A CN 201810614457 A CN201810614457 A CN 201810614457A CN 108805358 A CN108805358 A CN 108805358A
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Abstract
The invention discloses a kind of, and the Gray Dynamic based on multiple factors strengthens prediction technique, it is related to Gray Dynamic and strengthens electric powder prediction.Include the following steps：(1) to the dynamical system of N number of factor, N number of single-factor grey forecasting model is built；(2) certainty factor weight；(3) Gray Dynamic based on multiple factors strengthens prediction model.For the present invention when handling multiple-factor Dynamic System Forecast, effective dimensionality reduction greatly reduces the complexity of operation；This method has and has very strong followability to data, big particularly with quantity, the strong system of dynamic, has good prediction effect.
Description
Technical field
The present invention relates to Gray Dynamics to strengthen electric powder prediction, and in particular to a kind of ash based on multiple factors
Color dynamic contract-enhanced prediction technique.
Background technology
Gray system theory is born in nineteen eighty-two.In more than 30 years courses, either theoretical research, or application are ground
Study carefully, gray system theory all makes great progress.《Chinese library taxonomy》Gray system theory is classified as system
One of important content of science："N94Systematic science ... ..., N941.1General system theory ... ..., N941.5Gray system is managed
By ... ...." while gray system theory develops, the practical application of gray system theory is increasingly extensive, and application field is not
It is disconnected to expand, successively life science, environmental protection, electric power, IT, industry, agricultural, society, economy, the energy, traffic, geography,
Numerous science necks such as matter, oil, earthquake, meteorology, water conservancy, environment, ecology, medicine, sport, education, military affairs, the science of law, finance
Domain solves a large amount of practical problems in production, life and scientific research.
The coefficient faced at present belongs to complicated dynamical system more, is made of numerous factors.Based on the phase between multiple-factor
Mutual collective effect, structure prediction model is the main methods predicted at present for multiple-factor.But for dynamical system, because
Son also can be in dynamic change, and the prediction model built in this case, precision will receive influence.
It to a complicated research object, is analyzed according to multi-factor process, multiple factors are general in evolution process
There is certain to contact and influence each other, solves the problems, such as that system prediction preferably selects systematic cloud.Based on mean value (mean-
Value the systematic cloud for) generating sequential construction, is denoted as SCGMmvModel is reduced being kept for the while of being satisfied with accuracy
Calculation amount is suitable for higher to requirement of real-time major class multi-factor process.
It enablesFor original temporal, should mutually there are mean value gemerating time series
IfIt is satisfied with trend relational with nonhomogeneous index discrete function, then SCGMmv(1, h) model is
It solves (SCGMmv(1, h) prediction model) be
Or
In formula
The shortcomings that above-mentioned technology is：1, when the factor is more, high level matrix is formed, increases computational complexity.
2, the stronger system of dynamic, is based on multiple-factor collective effect, and precision sometimes can be than single-factor prediction respectively
It is low.
Invention content
In view of the shortcomings of the prior art, purpose of the present invention is to be to provide a kind of grey based on multiple factors
Dynamic contract-enhanced prediction technique avoids the high level matrix computational complexity being made of multiple-factor, and it is pre- also to promote multiple-factor dynamical system
Survey precision.
To achieve the goals above, the present invention is to realize by the following technical solutions：It is a kind of based on multiple factors
Gray Dynamic strengthens prediction technique, includes the following steps：(1) to the dynamical system of N number of factor, it is pre- to build N number of single-factor grey
Survey model；(2) certainty factor weight；(3) Gray Dynamic based on multiple factors strengthens prediction model.
The step (1) enablesFor original temporal, when should mutually have average generation
SequenceIfIt is satisfied with trend relational with nonhomogeneous index discrete function, then SCGMmv(1,1)
Model is
It solves (SCGMmv(1,1) prediction model) be
Or
In formula
The step (2) uses trend correlation analysis, determines N number of single-factor forecasting sequence and surveys the trend of sequence
The degree of association.By the size of trend correlation degree, the weight of N number of factor is determined.Referred to as measured value,Referred to as predict
Value, r, c ∈ { 1,2 ..., h } correspondingly,Sequence is referred to as surveyed,Referred to as forecasting sequence.
Wherein α, β ∈ [0,1], then claimTo simplify trend correlation degree.
The step (3) carries out forecast analysis to the dynamical system of multiple factors, can be carried out respectively to N single-factor
Forecast analysis is combined into the grey catheter based on multiple factors too in the size according to the trend correlation degree with actual measurement sequence
Strengthen prediction model.
The invention has the advantages that：
1, when handling multiple-factor Dynamic System Forecast, effective dimensionality reduction greatly reduces the complexity of operation；
2, this method has has very strong followability to data, big particularly with quantity, the strong system of dynamic, has very
Good prediction effect.
Description of the drawings
The following describes the present invention in detail with reference to the accompanying drawings and specific embodiments；
Fig. 1 is that the Gray Dynamic based on multiple factors of the present invention strengthens prediction technique block diagram.
Specific implementation mode
To make the technical means, the creative features, the aims and the efficiencies achieved by the present invention be easy to understand, tie below
Specific implementation mode is closed, the present invention is further explained.
Referring to Fig.1, present embodiment uses following technical scheme：A kind of Gray Dynamic based on multiple factors is strong
Change prediction technique, includes the following steps：(1) to the dynamical system of N number of factor, N number of single-factor grey forecasting model is built.
It enablesFor original temporal, should mutually there are mean value gemerating time seriesIfIt is satisfied with trend relational with nonhomogeneous index discrete function, then SCGMmv(1,1) mould
Type is
It solves (SCGMmv(1,1) prediction model) be
Or
In formula
(2) certainty factor weight.
With trend correlation analysis, determines N number of single-factor forecasting sequence and survey the trend correlation degree of sequence.It is logical
The size for crossing trend correlation degree determines the weight of N number of factor.Referred to as measured value,Referred to as predicted value,
R, c ∈ { 1,2 ..., h } correspondingly,Sequence is referred to as surveyed,Referred to as forecasting sequence.
Wherein α, β ∈ [0,1], then claimTo simplify trend correlation degree.
(3) Gray Dynamic based on multiple factors strengthens prediction model.
Forecast analysis is carried out to the dynamical system of multiple factors, can forecast analysis be carried out to N number of single-factor respectively, in root
According to the size of the trend correlation degree with actual measurement sequence, it is combined into the grey catheter based on multiple factors and strengthens very much prediction model.
As shown in Figure 1.
For present embodiment when handling multiple-factor Dynamic System Forecast, effective dimensionality reduction greatly reduces answering for operation
Miscellaneous degree.The Gray Dynamic based on multiple factors of present embodiment strengthen prediction technique have to data have it is very strong with
Casual, dynamic strong system big particularly with quantity promotes multiple-factor Dynamic System Forecast precision, has prediction well
Effect.
The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention.The skill of the industry
Art personnel it should be appreciated that the present invention is not limited to the above embodiments, the above embodiments and description only describe
The principle of the present invention, without departing from the spirit and scope of the present invention, various changes and improvements may be made to the invention, this
A little changes and improvements all fall within the protetion scope of the claimed invention.The claimed scope of the invention is by the attached claims
Book and its equivalent thereof.
Claims (4)
1. a kind of Gray Dynamic based on multiple factors strengthens prediction technique, which is characterized in that include the following steps：(1) to N number of
The dynamical system of the factor builds N number of single-factor grey forecasting model；(2) certainty factor weight；(3) ash based on multiple factors
Color dynamic contract-enhanced prediction model.
2. a kind of Gray Dynamic based on multiple factors according to claim 1 strengthens prediction technique, which is characterized in that institute
The step of stating (1) enablesFor original temporal, should mutually there are mean value gemerating time seriesIfIt is satisfied with trend relational with nonhomogeneous index discrete function, then SCGMmv(1,1) mould
Type is
It solves (SCGMmv(1,1) prediction model) be
Or
In formula
。
3. a kind of Gray Dynamic based on multiple factors according to claim 1 strengthens prediction technique, which is characterized in that institute
The step of stating (2) uses trend correlation analysis, determines N number of single-factor forecasting sequence and surveys the trend correlation degree of sequence.Pass through
The size of trend correlation degree determines the weight of N number of factor.Referred to as measured value,Referred to as predicted value, r, c ∈ 1,
2 ..., h } correspondingly,Sequence is referred to as surveyed,Referred to as forecasting sequence.
Wherein α, β ∈ [0,1], then claimTo simplify trend correlation degree.
4. a kind of Gray Dynamic based on multiple factors according to claim 1 strengthens prediction technique, which is characterized in that institute
The step of stating (3) carries out forecast analysis to the dynamical system of multiple factors, can carry out forecast analysis to N number of single-factor respectively,
According to the size of the trend correlation degree with actual measurement sequence, it is combined into the grey catheter based on multiple factors and strengthens very much prediction mould
Type.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112505128A (en) * | 2020-11-30 | 2021-03-16 | 北方民族大学 | Method and device for nondestructive testing of reducing sugar of wine |
| CN113177311A (en) * | 2021-04-25 | 2021-07-27 | 南京航空航天大学 | Press fitting quality prediction method based on gray Markov model |
-
2018
- 2018-06-14 CN CN201810614457.XA patent/CN108805358A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112505128A (en) * | 2020-11-30 | 2021-03-16 | 北方民族大学 | Method and device for nondestructive testing of reducing sugar of wine |
| CN113177311A (en) * | 2021-04-25 | 2021-07-27 | 南京航空航天大学 | Press fitting quality prediction method based on gray Markov model |
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