CN106296434A - A kind of Grain Crop Yield Prediction method based on PSO LSSVM algorithm - Google Patents
A kind of Grain Crop Yield Prediction method based on PSO LSSVM algorithm Download PDFInfo
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- CN106296434A CN106296434A CN201610684306.2A CN201610684306A CN106296434A CN 106296434 A CN106296434 A CN 106296434A CN 201610684306 A CN201610684306 A CN 201610684306A CN 106296434 A CN106296434 A CN 106296434A
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- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
Abstract
A kind of Grain Crop Yield Prediction method based on PSO LSSVM algorithm, the method in turn includes the following steps: obtain the grain yield in known time and the value of main affecting factors；The initial data of grain yield and main affecting factors is smoothed；Forecast model is drawn according to least square method supporting vector machine model；The width of penalty factor and kernel function is solved by iterative algorithm；Solve Lagrange multiplier and variate-value b；Solve the value of Radial basis kernel function；The Lagrange multiplier solved, variate-value b and RBF are substituted into forecast model, and calculates the predictive value of the grain yield of 1 year with model.The invention discloses a kind of Grain Crop Yield Prediction method based on PSO LSSVM algorithm, grain yield can be predicted by this Forecasting Methodology, it was predicted that precision is high, can improve the prediction accuracy of China's grain yield.
Description
Technical field
The invention belongs to Grain Crop Yield Prediction field, be specifically related to a kind of grain yield based on PSO-LSSVM algorithm pre-
Survey method.
Background technology
The Accurate Prediction of grain yield can be that government decision provides foundation, and grain yield data are typical small sample numbers
According to, it is vulnerable to the impact of the uncertain factor, shows as a complicated nonlinear system.
At present, the method for prediction grain yield is varied both at home and abroad, mainly includes regression analysis, Time Series Method
And Artificial Neural Network.The method can be applied to grain yield to carrying out causal analysis between variable by regression analysis
During prediction, the main affecting factors of grain yield can be found, but owing to all of factor of influence can not be carried out fully by the method
Consideration, be therefore only applicable to short-term forecast.Time Series Method calculates the shortest, relatively low to the quantitative requirement of historical data,
The consecutive variations of yield can be reflected.But the model that the method is set up is model based on linear data mostly, and during grain
Between sequence data tend to appear as nonlinear characteristic, therefore cause precision of prediction the highest.Artificial Neural Network is a kind of non-
Linear prediction method, has parallel processing and the fault-tolerant ability of height, and application is relatively broad at present.But, neutral net requirement
Data sample is big, and grain yield data belong to Small Sample Database, so often there is result over-fitting during prediction, extensive
The phenomenon such as indifferent.
To sum up, all there is either large or small problem in existing Grain Crop Yield Prediction method, and provides a kind of precision of prediction high
Grain Crop Yield Prediction method, grain prediction is had great importance.
Summary of the invention
It is desirable to provide a kind of Grain Crop Yield Prediction method based on PSO-LSSVM algorithm that precision of prediction is high.
For solving above-mentioned technical problem, the invention provides following technical scheme: a kind of based on PSO-LSSVM algorithm
Grain Crop Yield Prediction method, the method in turn includes the following steps:
(1) grain yield in known time and the value of main affecting factors are obtained；
(2) grain yield in the known time that step (1) gets and the initial data of main affecting factors are carried out smooth place
Reason；
(3) forecast model is drawn according to least square method supporting vector machine model, wherein
It is the grain yield of 1 year,It is the value of the factor of influence of 1 year,It it is the value of the factor of influence of 1 year；
Being Lagrange multiplier, b is variate-value,For Radial basis kernel function；
(4) penalty factor is solved by iterative algorithmWidth with kernel function；
(5) Lagrange multiplier and variate-value b are solved；
(6) Radial basis kernel function is solvedValue；
(7) Lagrange multiplier solved, variate-value b and RBF are substituted into forecast model
, and calculate with this modelThe predictive value of the grain yield of i.e. 1 year；Wherein i is
The grain yield that can inquire and the year of factor of influence value, 1 year main affecting factors is it is known that grain yield is to be predicted
's.
The method that draws of main affecting factors is: according to grain yield and the value of factor of influence, calculates factor of influence and grain
The degree of association between food yield, compare the degree of association between factor of influence and grain yield, and wherein the degree of association is maximum
Factor of influence is main affecting factors.
Described in step (2), the method that the grain yield in step (1) and main affecting factors are smoothed
For:
1) respectively the initial data of grain yield and main affecting factors is done difference processing；
2) according to the difference processing of step 1), grain yield and fluctuation meansigma methods p of main affecting factors and py are calculated respectively；
3) obtain grain yield and the value of main affecting factors after smoothing processing, use respectivelyLp-Lp1WithYp-Yp1Calculate adjacent two
Undulating value between annual grain yield and main affecting factors valuep1Withpy1；
Following three kinds of situations are had for grain yield:
The first situation:P1 ＞ p, andLp ＜ Lp1, then makeLp1=Lp+p；
The second situation:P1 ＞ p, andLp ＞ Lp1, then makeLp1=Lp-p；
The third situation:P1 ＜ p, thenLp1= Lp1；
Following three kinds of situations are had for the value of main affecting factors:
The first situation:Py1 ＞ py, andYp ＜ Yp1, then orderYp1=Yp+py；
The second situation:Py1 ＞ py, andYp ＞ Yp1, then orderYp1=Yp-py；
The third situation:Py1 ＜ py, thenYp1= Yp1；
WhereinLpIt isn-mThe grain yield in year；Lp1Forn-m+1The grain yield in year；YpIt isn-mAnnual main shadow
Ring the original value of the factor；Yp1Forn-m+1The original value of annual main affecting factors.
Penalty factor is solved by the method for iteration optimizingWidth with kernel function:
According to forecast model, step 1, show that fitness function is:
In formula,WithIt is respectively actual value and the predictive value of 1 year grain yield；WithIt is training sample and inspection sample respectively
This number；
Step 2, according to equation below, updates particle rapidity and position；
Wherein,
In formula,tFor iterations,Being nonnegative number, referred to as Inertia Weight, before how many control particle every generation speed renewals has
Speed remain.WithIt is nonnegative constant, referred to as accelerated factor.It is the speed of particle,,
It is constant, is set by the user the speed for limiting particle,It it is the position of particle.WithIt is random between [0,1]
Number.WithBe respectively according to training after LSSVM for the test error that test sample collection obtains determine when the one before
Body optimal value and current population optimal value；
Step 3, is analyzed the result after each iteration, the current fitness value obtained by this iterationAnd individuality
Extreme valueCompare, if>, then useReplace；With its fitness valueWith overall situation pole
ValueCompare, if>, then useReplacement is fallen, simultaneously according to speed and the position of step 3 more new particle；
Step 4, if fitness function value reaches precision of prediction or reaches default maximum iteration time, then output parameter is
Excellent solution, optimizing terminates, if the most above-mentioned condition, branches to step (3) and re-starts search.
The method for solving of Lagrange multiplier and variate-value b is: according to formula, solve b, root
According to formula, solve；Wherein,It is 1iThe inversion of matrix,It it is main affecting factors composition
'sSquare formation, wherein the value of the i-th row i row is xi×x i ,ForiRank unit matrix, yiIt it is the value of 1 year grain yield；For penalty factor.
The method for solving of RBF is: utilize formulaSolve, its
In,It is the value of the factor of influence of 1 year,It it is the value of the factor of influence of 1 year.
By above technical scheme, the invention have the benefit that
1, forecast model of the present invention is by getting up PSO algorithm and least square method supporting vector machine models coupling, simultaneously
Initial data is carried out smoothing processing, has been obviously improved so that precision of prediction has had, for improving Grain Crop Yield Prediction
Accuracy rate has great significance.
2, being determined by the value of main affecting factors, the prediction for this grain yield has had simplification largely.
3, penalty factorWidth with kernel functionMethod for solving in, by iteration optimization, thus draw optimum,
And then improve the precision of prediction of forecast model further.
Accompanying drawing explanation
Fig. 1 is Forecasting Methodology flow chart of the present invention.
Detailed description of the invention
A kind of Grain Crop Yield Prediction method based on PSO-LSSVM algorithm, as it is shown in figure 1, the method includes as follows successively
Step:
(1) obtain the grain yield in known time and the value of main affecting factors, it is known that time grain yield and mainly affect because of
The value of son can be recorded by existing document and learn.
Wherein judge that whether factor of influence is that the method for main affecting factors is: according to grain yield and factor of influence, meter
Calculating the degree of association between factor of influence and grain yield, it neutralizes the maximum factor of influence of the grain yield degree of association is main impact
The factor.The computational methods of the degree of association between factor of influence and grain yield are ripe prior art.Wherein, factor of influence and
The calculating of the degree of association between grain yield refers to the Chinese patent application of Application No. " 201510985352.1 ".
(2) grain yield in known time and the initial data of main affecting factors to obtaining are smoothed, flat
The method of sliding process is:
The first step, does difference processing to the initial data of grain yield and main affecting factors respectively；
Second step, according to the difference processing of step 1), calculates fluctuation meansigma methods p of grain yield and main affecting factors respectively
Fluctuation meansigma methods py；
3rd step, obtains grain yield and the value of main affecting factors after smoothing processing: calculate adjacent two year with Lp-Lp1
Undulating value p1 between grain yield, calculates adjacent two annual main affecting factors values py1 with Yp-Yp1；Wherein Lp is m
The grain yield in year；Lp1 is the grain yield in m+1 year；Yp is the original value of m year main affecting factors；Yp1 is m+
The original value (m=1,2,3 ... n-2) of 1 annual main affecting factors.
Following three kinds of situations are had for grain yield:
The first situation: if p1 is ＞ p, and Lp ＜ Lp1, then order Lp1=Lp+p；
The second situation: if p1 is ＞ p, and Lp ＞ Lp1, then order Lp1=Lp-p；
The third situation: if p1 ＜ p, then Lp1=Lp1；
Following three kinds of situations are had for the value of main affecting factors:
The first situation: if py1 is ＞ py, and Yp ＜ Yp1, then order Yp1=Yp+py；
The second situation: if py1 is ＞ py, and Yp ＞ Yp1, then order Yp1=Yp-py；
The third situation: if py1 ＜ py, then Yp1=Yp1；
Reduce grain yield and the undulating value of main affecting factors by smoothing processing, thus improve precision of prediction.
(3) forecast model is drawn according to least square method supporting vector machine model, whereinIt is the grain yield of 1 year,It is the value of the factor of influence of 1 year,It it is the value of the factor of influence of 1 year；Being Lagrange multiplier, b is variate-value,For Radial basis kernel function；This forecast model is according to
Little square law draws.
(4) penalty factor is solved by iterative algorithmWidth with kernel function；
Penalty factorWidth with kernel functionMethod for solving be:
Penalty factor is solved by the method for iteration optimizingWidth with kernel function:
According to forecast model, step 1, show that fitness function is:
In formula,WithIt is respectively actual value and the predictive value of 1 year grain yield；WithIt is training sample and inspection sample respectively
This number；
Step 2, according to equation below, updates particle rapidity and position；
Wherein,
In formula,tFor iterations,Being nonnegative number, referred to as Inertia Weight, before how many control particle every generation speed renewals has
Speed remain.WithIt is nonnegative constant, referred to as accelerated factor.It is the speed of particle,,
It is constant, is set by the user the speed for limiting particle,It it is the position of particle.WithIt is random between [0,1]
Number.WithBe respectively according to training after LSSVM for the test error that test sample collection obtains determine when the one before
Body optimal value and current population optimal value；
Step 3, is analyzed the result after each iteration, the current fitness value obtained by this iterationAnd individuality
Extreme valueCompare, if>, then useReplace；With its fitness valueWith overall situation pole
ValueCompare, if>, then useReplacement is fallen, simultaneously according to speed and the position of step 3 more new particle；
Step 4, if fitness function value reaches precision of prediction or reaches default maximum iteration time, then output parameter is
Excellent solution, optimizing terminates, if the most above-mentioned condition, branches to step (3) and re-starts search.
For random number rand (1,1), automatically generate for computer, not by man's activity during prediction, as long as having
The value of known main affecting factors and grain yield, it is possible to draw the cereal product of the unknown according to forecast model.
(5) solving Lagrange multiplier and variate-value b, the method for solving of Lagrange multiplier and variate-value b is: according to public affairs
Formula, solve b, according to formula(wherein i=1,2, n-1), solves；
Wherein,It is 1iThe inversion of matrix,It it is main affecting factors compositionSquare formation, wherein the value of the i-th row i row is xi , xi
It is the value of 1 year main affecting factors, yiIt it is the value of 1 year grain yield；For penalty factor.
(6) Radial basis kernel function is solvedValue, the method for solving of RBF is: utilize formulaSolve, wherein,It is the value of the factor of influence of 1 year,It it is the shadow of 1 year
Ringing the value of the factor, wherein n is that the value of main affecting factors is it is known that grain yield time to be predicted.WhereinDraw and can join
Examine the Chinese patent application of Application No. " 201510985352.1 ".
(7) Lagrange multiplier solved, variate-value b and RBF are substituted into formula
, and the predictive value of 1 year is calculated with this model；Wherein i is that the grain that can inquire produces
In the year of the value of amount and main affecting factors, the value of the main affecting factors of 1 year is it is known that grain yield is to be predicted.
According to above step, the grain yield of, 2012 and in 2011 in 2013 is predicted, utilized existing simultaneously
Some LS-SVM, SVM and ARIMA models have been also carried out prediction to this grain yield in 3 years, it was predicted that result is as shown in table 1 below:
Table 1 Grain Crop Yield Prediction results contrast
Predicting the outcome according to above-mentioned, actual value and above-mentioned predicting the outcome are compared, wherein forecast error is as shown in table 2:
Table 2 average relative error compares
By Tables 1 and 2, the precision of ARIMA forecast model is minimum, and average relative error is 1.73%, and SVM model has
Non-linear advantage so that improve a bit than ARIMA on precision of prediction, average relative error is 1.56%；LSSVM is owing to needing
The model parameter determined is fewer than SVM, has preferably coordinated the relation between the complication of model and generalization ability, it was predicted that precision is also
It is obviously improved；And PSO-LSSVM forecast model is by introducing particle swarm optimization algorithm, that finds in LSSVM model is optimal
Parameter, improves model accuracy again, it was predicted that the performance that result also show this model is the most superior, and average relative error is
0.8%, the Forecasting Methodology of the present invention by the initial data that PSO-LSSVM forecast model is used is smoothed, average phase
Minimum to error, it was predicted that effect is the most optimal.
The invention discloses a kind of Grain Crop Yield Prediction method based on PSO-LSSVM algorithm, can by this Forecasting Methodology
To be predicted grain yield, PSO algorithm and least square method supporting vector machine models coupling are got up by the method, simultaneously to former
Beginning data have carried out smoothing processing, it was predicted that precision is substantially better than traditional LSSVM, SVM and ARIMA model, can improve me
Predicting the outcome of state's grain yield, for grain, plants and purchases offer guidance.
Claims (6)
1. a Grain Crop Yield Prediction method based on PSO-LSSVM algorithm, it is characterised in that: the method includes walking as follows successively
Rapid:
Obtain the grain yield in known time and the value of main affecting factors；
The grain yield in known time and the initial data of main affecting factors that get step (1) are smoothed；
Forecast model is drawn according to least square method supporting vector machine model, whereinIt is
The grain yield of n,It is the value of the factor of influence of 1 year,It it is the value of the factor of influence of 1 year；It is to draw
Ge Lang multiplier, b is variate-value,For Radial basis kernel function；
Penalty factor is solved by iterative algorithmWidth with kernel function；
Solve Lagrange multiplier and variate-value b；
Solve Radial basis kernel functionValue；
The Lagrange multiplier solved, variate-value b and RBF are substituted into forecast model
, and calculate with this modelThe predictive value of the grain yield of i.e. 1 year；Wherein i is
The grain yield that can inquire and the year of factor of influence value, 1 year main affecting factors is it is known that grain yield is to be predicted
's.
2. Grain Crop Yield Prediction method based on PSO-LSSVM algorithm as claimed in claim 1, it is characterised in that: main shadow
The method that draws ringing the factor is: according to grain yield and the value of factor of influence, calculate the pass between factor of influence and grain yield
Connection degree, compares the degree of association between factor of influence and grain yield, and the factor of influence that wherein degree of association is maximum is main
Factor of influence.
3. Grain Crop Yield Prediction method based on PSO-LSSVM algorithm as claimed in claim 2, it is characterised in that: step (2)
Described, the method being smoothed the grain yield in step (1) and main affecting factors is:
Respectively the initial data of grain yield and main affecting factors is done difference processing；
According to the difference processing of step 1), calculate grain yield and fluctuation meansigma methods p of main affecting factors and py respectively；
Obtain grain yield and the value of main affecting factors after smoothing processing, use respectivelyLp-Lp1WithYp-Yp1Calculate adjacent 2 year
Undulating value between degree grain yield and main affecting factors valuep1Withpy1；
Following three kinds of situations are had for grain yield:
The first situation:P1 ＞ p, andLp ＜ Lp1, then makeLp1=Lp+p；
The second situation:P1 ＞ p, andLp ＞ Lp1, then makeLp1=Lp-p；
The third situation:P1 ＜ p, thenLp1= Lp1；
Following three kinds of situations are had for the value of main affecting factors:
The first situation:Py1 ＞ py, andYp ＜ Yp1, then orderYp1=Yp+py；
The second situation:Py1 ＞ py, andYp ＞ Yp1, then orderYp1=Yp-py；
The third situation:Py1 ＜ py, thenYp1= Yp1；
WhereinLpIt isn-mThe grain yield in year；Lp1Forn-m+1The grain yield in year；YpIt isn-mAnnual main shadow
Ring the original value of the factor；Yp1Forn-m+1The original value of annual main affecting factors.
4. Grain Crop Yield Prediction method based on PSO-LSSVM algorithm as claimed in claim 3, it is characterised in that: by repeatedly
Method for optimizing solves the width of penalty factor and kernel function:
According to forecast model, step 1, show that fitness function is:
In formula,WithIt is respectively actual value and the predictive value of 1 year grain yield；WithIt is training sample and inspection sample respectively
This number；
Step 2, according to equation below, updates particle rapidity and position；
Wherein,
In formula,tFor iterations,Being nonnegative number, referred to as Inertia Weight, before how many control particle every generation speed renewals has
Speed remain；
WithIt is nonnegative constant, referred to as accelerated factor；
It is the speed of particle,,It is constant,It it is the position of particle；
WithIt it is the random number between [0,1]；
WithBe respectively according to training after LSSVM current individual that the test error that test sample collection obtains is determined
The figure of merit and current population optimal value；
Step 3, is analyzed the result after each iteration, the current fitness value obtained by this iterationWith individual pole
ValueCompare, if>, then useReplace；With its fitness valueAnd global extremumCompare, if>, then useReplacement is fallen, simultaneously according to speed and the position of step 3 more new particle；
Step 4, if fitness function value reaches precision of prediction or reaches default maximum iteration time, then output parameter is
Excellent solution, optimizing terminates, if the most above-mentioned condition, branches to step (3) and re-starts search.
5. Grain Crop Yield Prediction method based on PSO-LSSVM algorithm as claimed in claim 4, it is characterised in that: glug is bright
The method for solving of day multiplier and variate-value b is: according to formula, solve b, according to formula, solve；Wherein,It is 1iThe inversion of matrix,It it is main affecting factors compositionSide
Battle array, wherein the value of the i-th row i row is xi×x i,ForiRank unit matrix, yiIt it is the value of 1 year grain yield；For punishment
The factor.
6. Grain Crop Yield Prediction method based on PSO-LSSVM algorithm as claimed in claim 5, it is characterised in that: radially base
The method for solving of function is: utilize formulaSolve, wherein,It it is the shadow of 1 year
Ring the value of the factor,It it is the value of the factor of influence of 1 year.
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