CN110414115A - A kind of wavelet neural network tomato yield prediction technique based on genetic algorithm - Google Patents

A kind of wavelet neural network tomato yield prediction technique based on genetic algorithm Download PDF

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CN110414115A
CN110414115A CN201910663789.1A CN201910663789A CN110414115A CN 110414115 A CN110414115 A CN 110414115A CN 201910663789 A CN201910663789 A CN 201910663789A CN 110414115 A CN110414115 A CN 110414115A
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王永刚
尹义志
刘宇航
张大鹏
刘潭
姜迎春
张楠楠
邓寒冰
苗腾
袁青云
栗庆吉
陈春玲
李征明
纪建伟
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Abstract

The wavelet neural network tomato yield prediction technique based on genetic algorithm that the invention discloses a kind of, belongs to technical field of agricultural information.This method first chooses a certain amount of parameter as input variable, passes through the classification, processing and the screening that carry out to these parameters and optimizes training to it to using genetic algorithm, obtains suitable data population.Then the number of nodes of network input layer, hidden layer and output layer is determined, replace the excitation function of the hiding node layer of BP neural network using wavelet basis function, and it introduces shift factor and scale factor is constantly adjusted weight, by repeatedly trained and iteration, making the error of model prediction constantly reduces, it is enabled to export result constantly close to measured value, by control errors in reasonable range, the precision of prediction and function convergence performance of model are improved, provides more reliable theories integration for crop yield in prediction greenhouse.

Description

A kind of wavelet neural network tomato yield prediction technique based on genetic algorithm
Technical field
The invention belongs to technical field of agricultural information, in particular to a kind of wavelet neural network tomato based on genetic algorithm Production prediction method.
Background technique
Tomato is one of the main vegetables of Northern Part of China facility cultivation, the indoor tomato planting of northern area sunlight The cultivation mode of efficient crop rotation is mostly used, the physiological property of the environmental factor in greenhouse, the nutrient content of soil and tomato is directly determined The yield of tomato is determined.The indoor tomato yield of prediction temperature can be to determine suitable planting scheme, reasonable arrangement dose, Irrigation volume, takes the measures such as deinsectization, weeding in time, periodically takes ventilation, heats, the measures such as light filling, is the healthy growth of tomato It creates a favorable environment, and lays the foundation for the good quality and high output of greenhouse tomato.The research that forefathers predict tomato yield is very wide It is general, but be mostly focused on and the indoor tomato yield of temperature is predicted using crop mechanism model or crop growth model, The parameter being related to is relatively fewer, and calculation method has certain limitation.Using genetic algorithm in conjunction with wavelet neural network Mode carries out the prediction of greenhouse tomato yield, can be according to historical data over the years, and passes through each in continuous correction model Kind parametric variable improves and Optimized model, lift scheme precision of prediction make it be more applicable for the actual rings of Greenhouse in North Border has more wide application prospect.
Summary of the invention
The present invention provides a kind of wavelet neural network tomato yield prediction technique based on genetic algorithm, can predict the north Tomato yield in energy saving sunlight greenhouse can be determining suitable planting scheme.
A kind of wavelet neural network tomato yield prediction model based on genetic algorithm, which is characterized in that this model it is defeated Entering variable includes following characteristics parameter: environment temperature;Ambient humidity;Irrigation volume;Nitrogenous fertilizer input amount;Phosphate fertilizer input amount;Potash fertilizer is thrown Enter amount;CO2Concentration;Intensity of illumination;
The model is established by following steps:
An initial population X is generated at randomm×n:
N=s1×s2+s2×s3+s2+s3 (1)
Wherein: m is initial population quantity, and n is individual lengths, and individual lengths had both represented the genic value quantity of each individual, Also the initial weight quantity an of neural network is represented;s1For input layer number;s2For node in hidden layer;s3For output layer Number of nodes;
The output error value E that genetic algorithm passes through each individual in calculating initial populationi, fitness value fi, and according to individual The value of fitness value is assessed, and individual of the fitness value in preset range A in initial population is selected to continue into sub- population Optimize training:
In sub- population, i-th of individual intersect or the probability of mutation operation is pi, according to crossing-over rate pcAnd aberration rate pmAuto-adaptive function come judge the individual whether carry out intersect or genetic manipulation:
In formula: kc、kmIt is the real number less than 1, fcFor the ideal adaptation angle value to be intersected, fmIt is suitable for the individual to be made a variation Answer angle value, fmaxMaximum adaptation angle value and average fitness value respectively in population,For the degree of convergence of population;
The data that input variable large deviations are more than its annual mean ± 10% are rejected, using genetic algorithm to data-optimized, Data dynamic range≤± 5% is considered as that treatment effect is preferable, will be by screening with the data after optimization processing as wavelet neural The input data of network simultaneously carries out subsequent arithmetic;
Treated characteristic parameter will be passed through as the input of wavelet-neural network model, greenhouse tomato yield conduct Output, wherein include the following steps:
The mother wavelet function of the model selection are as follows:
G (x)=cos (1.75x) exp (- x2/2) (7)
Mother wavelet function is subjected to scale and translation transition structure wavelet basis function:
Wherein, aj、bjThe scale factor and shift factor of respectively j-th hidden layer node;
ykFor the output of model:
Wherein, xi(i=1,2 ..., I) is the input of i-th of node of input layer, yk(j=1,2 ..., k) be output layer The output of j-th of node;
Define error function E are as follows:
Wherein, ykFor the reality output of k-th of node of output layer, tkIt is exported for the target of k-th of node of output layer;
Adjustment to weight, scale factor, shift factor includes:
Wherein, learning rate: η (η > 0), factor of momentum: μ (0 < μ < 1).
More preferably, error amount is expressed as a percentage, then fitness value is in 0~1 range, A=fi> 0.667.
The present invention provides a kind of wavelet neural network tomato yield prediction technique based on genetic algorithm, by data into Capable classification, processing and screening simultaneously optimize training to it using genetic algorithm, obtain suitable data population.Then it determines The number of nodes of network input layer, hidden layer and output layer replaces BP neural network to hide swashing for node layer using wavelet basis function Function is encouraged, and introduces shift factor and scale factor is constantly adjusted weight, by repeatedly trained and iteration, makes model The error of prediction constantly reduces, and enables its output result constantly close to measured value, can be by control errors in reasonable range.
Detailed description of the invention
Fig. 1 is a kind of small echo of the wavelet neural network tomato yield prediction technique based on genetic algorithm provided by the invention Neural network structure figure;
Fig. 2 is GA-WNN forecast result of model figure;
Fig. 3 is GA-WNN model error percentage curve;
Fig. 4 is WNN forecast result of model figure;
Fig. 5 is WNN model error percentage curve;
Fig. 6 is BP neural network forecast result of model;
Fig. 7 is BP neural network model error percentage curve;
Fig. 8 is a kind of process of the wavelet neural network tomato yield prediction technique based on genetic algorithm provided by the invention Figure.
Specific embodiment
With reference to the accompanying drawing, the specific embodiment of the present invention is described in detail, it is to be understood that of the invention Protection scope be not limited by the specific implementation.
The establishment process of tomato yield prediction model: first to data carry out classification, processing and screening and to application lose Propagation algorithm optimizes training to it, obtains suitable data population.Then network input layer, hidden layer and output layer are determined Number of nodes replaces BP neural network to hide the excitation function of node layer, and introduces shift factor and ruler using wavelet basis function The degree factor is constantly adjusted weight, by repeatedly trained and iteration, reduces the error of model prediction constantly, enables its output As a result constantly close to measured value, by control errors in reasonable range, detailed process is as shown in Figure 8.
GA-WNN algorithm specifically executes step:
This model chooses following parameter as input variable: 1. environment temperature;2. ambient humidity;3. irrigation volume;4. nitrogenous fertilizer Input amount;5. phosphate fertilizer input amount;6. potash fertilizer input amount;7. CO2 concentration;8. intensity of illumination, using tomato yield data as the mould The output variable of type.Firstly, optimizing processing using genetic algorithm as the preposition means of WNN algorithm to initial data, obtaining The data of dynamic range smaller (dynamic range of data≤± 5%), will be by screening with the data after optimization as subsequent The initial value of WNN algorithm.Then, traditional BP neural network is improved using wavelet function, by more points of wavelet function Distinguish that the characteristic of analysis is combined with the adaptive ability of neural network, wavelet structure neural network model, and it is constantly corrected With it is perfect, just greatly improve the precision of prediction and function convergence performance of model in this way, for prediction greenhouse in crop yield mention For more reliable theories integration.
Specific operation process is as follows:
(1) screening and optimization processing are carried out to 8 kinds of selected parameters using genetic algorithm.Firstly, generating one at random Initial population Xm×nProcessing is optimized to initial data, wherein m be initial population quantity (because have chosen 8 kinds of variables, Take m=8), individual lengths n had both represented the genic value quantity of each individual, also represented the initial weight quantity an of neural network (hidden layer number of nodes can constantly change with network training, and weight quantity also changes correspondingly, initially set 8 input layers, 5 hiding node layers, 1 output node layer, initial weight quantity are 46) and genic value in individual and neural network Initial weight corresponds.This research encodes genic value using real coding mode, can avoid decoding process, improves Training effectiveness.
N=s1×s2+s2×s3+s2+s3 (1)
In formula: n is individual lengths;s1For input layer number;s2For node in hidden layer;s3For output layer number of nodes.
The output error value E that genetic algorithm passes through each individual in calculating initial populationi, fitness value fi, and according to individual The value of fitness value is assessed, and selects in initial population that larger (error amount is expressed as a percentage fitness value, then fitness value In 0~1 range, by fiThe value of > 0.667 is considered as the preferable numerical value of fitness) individual enter sub- population continue it is excellent Change training.
In sub- population, the selected intersection or the probability of mutation operation of carrying out of i-th of individual is pi, and according to crossing-over rate pcWith aberration rate pmAuto-adaptive function come judge the individual whether carry out intersect or genetic manipulation, pcWith pmValue can root According to individual fitness fiValue and change adaptive response, keep population that there is diversity always with this.
In formula: kc、kmIt is the real number less than 1, fcFor the ideal adaptation angle value to be intersected, fmIt is suitable for the individual to be made a variation Answer angle value, fmaxMaximum adaptation angle value and average fitness value respectively in population,For the degree of convergence of population.According to Upper genetic algorithm accordingly carries out screening and optimization processing to initial data, and rejecting deviation is larger, and (certain variable is more than its annual Value ± 10% is accordingly to be regarded as the biggish value of deviation, and constrains its value with this bound, is defined as boundary value and carries out subsequent meter Calculate), using genetic algorithm to data-optimized, the dynamic range of data can be made to reduce, data dynamic range≤± 5% is considered as place Reason effect is preferable, as the input data of wavelet neural network and carries out subsequent fortune with the data after optimization processing for by screening It calculates.
(2) it handles by upper step with 8 kinds of characteristic parameters after optimization as the defeated of wavelet neural network (WNN) model Enter, for greenhouse tomato yield as output, the structure of network is most important for the precision of prediction of model, stability.Construct model When first have to consider the structure of model, can meet test requirements document, guarantee model stability and reliability, then application misses Difference function analysis there are the problem of, continue Optimized model, promoted precision of prediction.
The mother wavelet function of the model selection are as follows:
G (x)=cos (1.75x) exp (- x2/2) (7)
Mother wavelet function is subjected to scale and translation transition structure wavelet basis function:
Wherein, aj、bjThe scale factor and shift factor of respectively j-th hidden layer node.ykFor the output of model:
xiThe input of (i=1,2 ..., I) i-th of node of-input layer, yk(j=1,2 ..., k)-j-th of output layer section The output of point.
By being constantly adjusted to the weight in this model, scale factor, shift factor, the stability of lift scheme and Precision of prediction enables this model be more applicable for actual site environment, and constantly improve and optimize the model.Define error letter Number E are as follows:
Wherein, ykFor the reality output of k-th of node of output layer, tkIt is exported for the target of k-th of node of output layer.To power The adjustment of value, scale factor, shift factor includes:
Wherein, learning rate: η (η > 0), factor of momentum: μ (0 < μ < 1).By constantly being adjusted to the parameter in formula It is whole, thus the precision of prediction of lift scheme, and emulated using MATLAB software, verify the prediction effect of model.
Then by the parameters such as GA parameter, BP parameter, weight, scale factor, shift factor in this model constantly into Row adjustment, the stability and precision of prediction of lift scheme enable this model be more applicable for actual site environment, and constantly complete It is apt to and optimizes the model.
The data that this test is selected 2010~2015 years are trained model, examine using 2016~2018 years data Test the forecast result of GA-WNN model.This network is trained using 6 years data, the continuous weight for adjusting model, The model parameters such as scale factor, shift factor, learning rate, momentum factor and the number of iterations, simultaneously by multiple comparison test result Error is calculated, constantly makes improvements and optimizes, obtained the preferable model of prediction effect, and its error is in reasonable model In enclosing (percentage error is within 5%), predicted value differs very little with measured value, realizes precisely to greenhouse tomato yield substantially Prediction, the prediction effect of the GA-WNN model such as following figure.The result shows that this model predication value is preferable to the tracking effect of measured value, And greenhouse tomato yield can be effectively predicted in the high stability of model.Experimental data and comparison of test results table are as follows:
In order to verify the prediction effect of model, GA-WNN model and WNN model, BP neural network model are compared, Prediction effect comparative situation see the table below.Through analysis it is found that predicted value and actual value using two kinds of models have certain error, GA-WNN model average relative error is that 0.66%, WNN model average relative error is 1.02%, and BP neural network model is put down Equal relative error is that 2.42%, GA-WNN forecast result of model is ideal.GA-WNN model convergence rate is better than WNN and BP mind Through network model, had reached by 208 step prediction effects optimal.In conclusion by by genetic algorithm, wavelet analysis and BP The convergence rate that neural network is combined the GA-WNN model constructed is very fast, precision of prediction is higher, realizes tomato yield Accurate prediction, can for the indoor crop species of reasonable arrangement temperature, formulate irrigate and fertilising etc. governing plans foundation be provided.
This research and utilization GA-WNN model predicts the tomato yield in Greenhouse in North, has comprehensively considered influence The factor of greenhouse tomato yield is handled and is screened to parameter using means such as genetic algorithms, and the feature of data is then extracted Vector constructs greenhouse tomato Production Forecast Models as mode input, and utilizes the wavelet neural network mesoscale factor and translation The factor carries out weighed value adjusting, the precision of prediction of lift scheme.Simulation result shows that GA-WNN model average relative error is 0.66%, WNN model average relative error are 1.02%, and the average relative error of BP neural network model is 2.42%, GA- WNN forecast result of model is better than WNN and BP neural network model, has reached by 208 step prediction effects optimal, it was demonstrated that GA- WNN model has preferable practical application value.Compared with traditional WNN and BP neural network model, GA-WNN model is put down Relative error is smaller, precision is higher, stability is stronger, more meets greenhouse tomato production forecast, can be tomato species in greenhouse The formulation for planting decision provides foundation, and provides measures such as irrigation, fertilising, light filling and the ventilations taken during tomato planting Certain theories integration, and facilitation is played to the improvement and optimization of Greenhouse System.
Disclosed above is only several specific embodiments of the invention, and still, the embodiment of the present invention is not limited to this, is appointed What what those skilled in the art can think variation should all fall into protection scope of the present invention.

Claims (2)

1. a kind of wavelet neural network tomato yield prediction model based on genetic algorithm, which is characterized in that the input of this model Variable includes following characteristics parameter: environment temperature;Ambient humidity;Irrigation volume;Nitrogenous fertilizer input amount;Phosphate fertilizer input amount;Potash fertilizer investment Amount;CO2Concentration;Intensity of illumination;
The model is established by following steps:
An initial population X is generated at randomm×n:
N=s1×s2+s2×s3+s2+s3 (1)
Wherein: m is initial population quantity, and n is individual lengths, and individual lengths had both represented the genic value quantity of each individual, also generation The initial weight quantity of one neural network of table;s1For input layer number;s2For node in hidden layer;s3To export node layer Number;
The output error value E that genetic algorithm passes through each individual in calculating initial populationi, fitness value fi, and according to ideal adaptation The value of angle value is assessed, and individual of the fitness value in preset range A in initial population is selected to continue into sub- population Optimization training:
In sub- population, i-th of individual intersect or the probability of mutation operation is pi, according to crossing-over rate pcWith aberration rate pmFrom Fitness function come judge the individual whether carry out intersect or genetic manipulation:
In formula: kc、kmIt is the real number less than 1, fcFor the ideal adaptation angle value to be intersected, fmFor the individual adaptation degree to be made a variation Value, fmaxMaximum adaptation angle value and average fitness value respectively in population,For the degree of convergence of population;
The data that input variable large deviations are more than its annual mean ± 10% are rejected, using genetic algorithm to data-optimized, data Dynamic range≤± 5% is considered as that treatment effect is preferable, will be by screening with the data after optimization processing as wavelet neural network Input data and carry out subsequent arithmetic;
Using by treated characteristic parameter, as the input of wavelet-neural network model, greenhouse tomato yield is as defeated Out, wherein include the following steps:
The mother wavelet function of the model selection are as follows:
G (x)=cos (1.75x) exp (- x2/2) (7)
Mother wavelet function is subjected to scale and translation transition structure wavelet basis function:
Wherein, aj、bjThe scale factor and shift factor of respectively j-th hidden layer node;
ykFor the output of model:
Wherein, xi(i=1,2 ..., I) is the input of i-th of node of input layer, yk(j=1,2 ..., k) be j-th of output layer The output of node;
Define error function E are as follows:
Wherein, ykFor the reality output of k-th of node of output layer, tkIt is exported for the target of k-th of node of output layer;
Adjustment to weight, scale factor, shift factor includes:
Wherein, learning rate: η (η > 0), factor of momentum: μ (0 < μ < 1).
2. a kind of wavelet neural network tomato yield prediction technique based on genetic algorithm as described in claim 1, feature It is, error amount is expressed as a percentage, then fitness value is in 0~1 range, A=fi> 0.667.
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