CN108319134A - A kind of greenhouse environment intelligent control method based on extreme learning machine network - Google Patents
A kind of greenhouse environment intelligent control method based on extreme learning machine network Download PDFInfo
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- CN108319134A CN108319134A CN201810035493.0A CN201810035493A CN108319134A CN 108319134 A CN108319134 A CN 108319134A CN 201810035493 A CN201810035493 A CN 201810035493A CN 108319134 A CN108319134 A CN 108319134A
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- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
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Abstract
A kind of greenhouse environment intelligent control method based on extreme learning machine network, is related to greenhouse environment intelligent control method, and the method includes following preparation process：The collection of sample data according to the empirical data for certain crop growth that agricultural experts are provided, then passes through experiment, deviation of the acquisition crops in different growth periods；The normalized of sample data, the neural network model of foundation；Establish single extreme learning machine network-control model：Extreme learning machine network is made of input layer, hidden layer and output layer；Establish multiple limits learning machine network-control model：Multiple extreme learning machine network models are combined, multiple limits learning machine network-control model is constituted；Intelligent control is carried out to greenhouse using multiple limits learning machine network-control model, obtains green house control parameter.The present invention, which realizes, accurately controls crops in greenhouse growing environment, improves the yield and quality of crops in greenhouse.
Description
Technical field
The present invention relates to one kind, more particularly to a kind of greenhouse intelligent control side based on extreme learning machine network
Method.
Background technology
China is large agricultural country, and cultivated area of the greenhouse in China is very wide.Greenhouse can be crop growth
Suitable environment is provided, crops is made to avoid the influence of harsh weather.Intelligent plantation is realized using greenhouse, can be improved
The yield and quality of crops.China's tradition greenhouse production process needs a large amount of labour personnel to participate in, by experience management.
In fact, in the growth course of crops, temperature, humidity, intensity of illumination, CO2The factors such as concentration are to influence crop growth
Key factor.In order to improve the yield and quality of crops, it is necessary to the temperature in greenhouse, humidity, CO2Concentration etc.
Greenhouse environment parameter carries out accurate real-time control, and plant is made to be grown in always within the scope of best environmental parameter.
Currently, there are many method for environment data controlling for greenhouse, but there is control accuracies mostly high not enough, control
The problems such as effect is not ideal enough.Based on the greenhouse environment intelligent control method of extreme learning machine network, by extreme learning machine nerve
In greenhouse flower, control accuracy is higher for network application, and control effect is preferable.
Invention content
The purpose of the present invention is to provide a kind of greenhouse environment intelligent control method based on extreme learning machine network, this hairs
The bright site environment parameter by acquisition crop growth, and compare with the standard environment parameter of crop growth, it establishes
Extreme learning machine network control algorithm according to plant growth；It realizes and greenhouse environment parameter is accurately controlled.
The purpose of the present invention is what is be achieved through the following technical solutions：
A kind of greenhouse environment intelligent control method based on extreme learning machine network, the method includes following preparation process：
1）The collection of sample data：According to the empirical data for certain crop growth that agricultural experts are provided, then by testing,
Crops are obtained in the practical intensity of illumination of different growth periods and deviation, the practical soil moisture and the warp of experience intensity of illumination
Test the deviation of the soil moisture, the deviation of practical soil moisture and experience soil moisture, actual air temperature and experience air themperature
Deviation, the deviation of actual air humidity and experience air humidity, practical gas concentration lwevel and experience gas concentration lwevel
Deviation；
2）The normalized of sample data：By sampled dataIt normalizes in [0,1] section and is：
Wherein,For i-th of sampled data after normalized,For the minimum value of r sampled data,
For the maximum value of r sampled data；The neural network model of foundation；
3）Establish single extreme learning machine network-control model：Extreme learning machine network is made of input layer, hidden layer and output layer；
4）Establish multiple limits learning machine network-control model：Multiple extreme learning machine network models are combined, are constituted more
Extreme learning machine network-control model；
5）Intelligent control is carried out to greenhouse using multiple limits learning machine network-control model：Obtained each monopole is limited
The weighting coefficient of learning machine network brings multiple limits learning machine network model into, when the input of multiple limits learning machine network model is farming
The different time of object growth and the intensity of illumination deviation data of corresponding each time, soil moisture deviation data, air themperature are inclined
The input sample data of difference data, air humidity deviation data, gas concentration lwevel deviation data as extreme learning machine network
When, it can calculate cascade control and regulation amount using the built-up pattern, fan control regulated quantity, light compensating lamp control and regulation amount, dissipate
Hot device control and regulation amount, sunshading board control and regulation amount, motor control and regulation amount of leaking informaton；To cascade control and regulation amount, fan control
Regulated quantity, light compensating lamp control and regulation amount, radiator control and regulation amount, sunshading board control and regulation amount, motor control and regulation amount of leaking informaton
After each numerical value carries out anti-normalization processing, green house control parameter can be obtained.
A kind of greenhouse environment intelligent control method based on extreme learning machine network, the collection of the sample data
By intensity of illumination deviation data, soil moisture deviation data, the sky of the different time of crop growth and corresponding Each point in time
Temperature degree deviation data, the input of air humidity deviation data, gas concentration lwevel deviation data as extreme learning machine network
Sample data.
A kind of greenhouse environment intelligent control method based on extreme learning machine network, the collection of the sample data
By the corresponding cascade control and regulation amount of each deviation data of different time, fan control regulated quantity, light compensating lamp control and regulation amount,
Radiator control and regulation amount, sunshading board control and regulation amount, sample of the motor control and regulation amount of leaking informaton as extreme learning machine network
Output data.
A kind of greenhouse environment intelligent control method based on extreme learning machine network, the normalizing of the sample data
Change is handled, and r sample data after normalized is divided into two groups：One group as the common first of each single neural network model
Beginning training dataset is respectively trained each single extreme learning machine network；Another group is used as verification data collection, for verifying
The neural network model of foundation.
Advantages of the present invention is with effect：
Extreme learning machine network is applied in the control of greenhouse environment parameter by the present invention, according to the rule of plant growth, realizes
To the intelligent control of greenhouse environment parameter, crops in greenhouse growing environment is accurately controlled to realize, improves temperature
The yield and quality of room crops.
Description of the drawings
Fig. 1 is single extreme learning machine network-control model topology structure chart；
Fig. 2 is multiple limits learning machine network model topology diagram.
Specific implementation mode
The following describes the present invention in detail with reference to examples.
The present invention is realized by following steps：
One, the collection of sample data
According to the empirical data for certain crop growth that agricultural experts are provided, including needed for the different growth period of crops
Intensity of illumination, the soil moisture, soil moisture, air themperature, air humidity, the gas concentration lwevel data wanted.Again by testing,
Crops are obtained in the practical intensity of illumination of different growth periods and deviation, the practical soil moisture and the warp of experience intensity of illumination
Test the deviation of the soil moisture, the deviation of practical soil moisture and experience soil moisture, actual air temperature and experience air themperature
Deviation, the deviation of actual air humidity and experience air humidity, practical gas concentration lwevel and experience gas concentration lwevel
Deviation；And according to experiment, corresponding different intensity of illumination deviation data, soil moisture deviation data, soil moisture deviation are obtained
Cascade under data, air themperature deviation data, air humidity deviation data, gas concentration lwevel deviation data controls to adjust
Amount, fan control regulated quantity, light compensating lamp control and regulation amount, radiator control and regulation amount, sunshading board control and regulation amount, leak informaton motor
Control and regulation amount.In these data obtained, by the illumination of the different time of crop growth and corresponding Each point in time
Strength variance data, soil moisture deviation data, air themperature deviation data, air humidity deviation data, gas concentration lwevel
Input sample data of the deviation data as extreme learning machine network；By the corresponding cascade control of each deviation data of different time
Regulated quantity processed, light compensating lamp control and regulation amount, radiator control and regulation amount, sunshading board control and regulation amount, is put at fan control regulated quantity
Sample output data of the wind turbine control and regulation amount as extreme learning machine network.
Two, the normalized of sample dataIt normalizes in [0,1] section and is：
Wherein,For i-th of sampled data after normalized,For the minimum value of r sampled data,
For the maximum value of r sampled datas.
R sample data after normalized is divided into two groups：One group as the common of each single neural network model
Initial training data set is respectively trained each single extreme learning machine network；Another group is used as verification data collection, for verifying
The neural network model established.
Three, single extreme learning machine network-control model is established
Extreme learning machine network is made of input layer, hidden layer and output layer.Input layer variable is, right respectively
The parameter answered is crop growth time, the deviation of practical intensity of illumination and experience intensity of illumination, the practical soil moisture and experience
The deviation of the soil moisture, the deviation of practical soil moisture and experience soil moisture, actual air temperature and experience air themperature
Deviation, the deviation of actual air humidity and experience air humidity, practical gas concentration lwevel are inclined with experience gas concentration lwevel
Difference；Hidden layer has L number of nodes；It is cascade control and regulation amount, fan that output layer, which has 6 nodes, corresponding control parameter,
Control and regulation amount, light compensating lamp control and regulation amount, radiator control and regulation amount, sunshading board control and regulation amount, the motor that leaks informaton control are adjusted
Section amount.
Its output is denoted asIndicate maximum sampling number.Indicate ith sampling
J-th of parameter value.
Algorithmic procedure is as follows：
Take training set, whereinFor j-th of input sample data,For j-th of output sample
Data, select activation primitive for, it is 100 to select maximum node in hidden layer, it is expected that it is 0.02 to learn precision.
The determination method of the number of hidden nodes is as follows：
（1）Enable initial the number of hidden nodes；Ith for j-th of parameter is adopted
Sample value.
（2）Increase a hidden node, the number of hidden nodes;
（3）Weights are assigned at random to the hidden node newly increased;
（4）It is calculated to the node newly increased and exports weights, before updateThe output weights of a hidden node：;
（5）It calculates and increases by theError after a hidden node;
（6）WhenOr 2 norms of error EWhen, then stop, otherwise, turning（2）Continue.
After the determination of hidden layer node number, calculates network and export weights
This process is divided into following several steps：
（1）The random input layer that generates is to the weights of hidden layerWith each neuron threshold value of hidden layer；
（2）Calculate hidden layer output matrix
（3）Network hidden layer is calculated to the weights of output layer
WhenWhen being nonsingular,
WhenWhen being nonsingular,
Four, multiple limits learning machine network-control model is established
The present invention combines multiple extreme learning machine network models, constitutes multiple limits learning machine network-control model.It is more
Total output of extreme learning machine network-control model is the weighted sum of each single neural network output, i.e.,：
The wherein input data matrix of X multiple neural networks,For multiple neural network output vector,For multiple neural network
Prediction model,For single extreme learning machine network number,It isA list extreme learning machine Network Prediction Model,For
TheThe non-negative weight vector of a list neural network,, meet regression nature, i.e.,：。
The present invention is for weight distributions such as the determination uses of each weight coefficient, i.e.,。
Five, intelligent control is carried out to greenhouse using multiple limits learning machine network-control model
It brings the weighting coefficient of obtained each single extreme learning machine network into multiple limits learning machine network model, works as multiple limits
The input of learning machine network model is intensity of illumination deviation data, the soil of the different time and corresponding each time of crop growth
Temperature deviation data, air themperature deviation data, air humidity deviation data, gas concentration lwevel deviation data are as the limit
When the input sample data of habit machine network, cascade control and regulation amount can be calculated using the built-up pattern, fan control is adjusted
Amount, light compensating lamp control and regulation amount, radiator control and regulation amount, sunshading board control and regulation amount, motor control and regulation amount of leaking informaton；To water
Curtain control and regulation amount, fan control regulated quantity, light compensating lamp control and regulation amount, radiator control and regulation amount, sunshading board control to adjust
It measures, after each numerical value progress anti-normalization processing of motor control and regulation amount of leaking informaton, green house control parameter can be obtained.
Embodiment：
The collection of sample data according to the empirical data for certain crop growth that agricultural experts are provided, and according to experiment, obtains
To intensity of illumination deviation data, soil moisture deviation data, the sky of the different time and corresponding Each point in time of crop growth
Temperature degree deviation data, the input of air humidity deviation data, gas concentration lwevel deviation data as extreme learning machine network
Sample data；By the corresponding cascade control and regulation amount of each deviation data of different time, fan control regulated quantity, light compensating lamp control
Regulated quantity processed, radiator control and regulation amount, sunshading board control and regulation amount, motor control and regulation amount of leaking informaton are as extreme learning machine net
The sample output data of network.
The normalized of sample data, by sampled dataIt normalizes in [0,1] section and is：
Wherein,It isA sampled data after normalized,For the minimum value of 200 sampled datas,For the maximum value of 200 sampled datas.
200 sample datas after normalized are divided into two groups：One group of 150 sample data is limited as each monopole
The common initial training data set of learning machine network model is respectively trained each single extreme learning machine network；Another group 50
A sample data, as verification data collection, for verifying established neural network model.
Single extreme learning machine network-control model is established, the corresponding parameter of extreme learning machine network input layer is crop
Growth time, the deviation of practical intensity of illumination and experience intensity of illumination, the deviation of the practical soil moisture and the experience soil moisture,
The deviation of practical soil moisture and experience soil moisture, actual air temperature and the deviation of experience air themperature, actual air are wet
Deviation, the deviation of practical gas concentration lwevel and experience gas concentration lwevel of degree and experience air humidity；Output layer has 6 sections
Point, corresponding control parameter are cascade control and regulation amount, fan control regulated quantity, light compensating lamp control and regulation amount, radiator
Control and regulation amount, sunshading board control and regulation amount, motor control and regulation amount of leaking informaton.
Algorithmic procedure is as follows：
Take training set, whereinFor j-th of input sample data,It is j-th
Export sample data, select activation primitive for, it is 100 to select maximum node in hidden layer, it is expected that learning
It is 0.02 to practise precision.
The determination method of the number of hidden nodes is as follows：
（1）Enable initial the number of hidden nodes, error;
（2）Increase a hidden node, the number of hidden nodes;
（3）Weights are assigned at random to the hidden node newly increased;
（4）It is calculated to the node newly increased and exports weights；
Wherein,
（5）Before updateThe output weights of a hidden node：;
（6）It calculates and increases by theError after a hidden node;
（7）WhenOr 2 norms of error EWhen, then stop, otherwise, turning（2）Continue.
After the determination of hidden layer node number, network exports weightsCalculating process it is as follows：
This process is divided into following several steps：
（1）The random input layer that generates is to the weights of hidden layerWith each neuron threshold value of hidden layer；
（2）Calculate hidden layer output matrix
（3）Network hidden layer is calculated to the weights of output layer
WhenWhen being nonsingular,
WhenWhen being nonsingular,
,
Multiple limits learning machine network-control model is established, the present embodiment combines 3 single extreme learning machine network models,
Constitute multiple limits learning machine network-control model.Total output of multiple limits learning machine network-control model is that each single neural network is defeated
The weighted sum gone out, i.e.,：
In the present embodiment, weight coefficient。
Intelligent control is carried out to greenhouse using multiple limits learning machine network-control model, by obtained each monopole
The weighting coefficient of limit learning machine network brings multiple limits learning machine network model into, when the input of multiple limits learning machine network model is agriculture
The different time of plant growth and the intensity of illumination deviation data of corresponding each time, soil moisture deviation data, air themperature
The input sample number of deviation data, air humidity deviation data, gas concentration lwevel deviation data as extreme learning machine network
According to when, using the built-up pattern can calculate cascade control and regulation amount, fan control regulated quantity, light compensating lamp control and regulation amount,
Radiator control and regulation amount, sunshading board control and regulation amount, motor control and regulation amount of leaking informaton；To cascade control and regulation amount, fan control
Regulated quantity processed, light compensating lamp control and regulation amount, radiator control and regulation amount, sunshading board control and regulation amount, the motor that leaks informaton control to adjust
After measuring each numerical value progress anti-normalization processing, green house control parameter can be obtained.
Claims (4)
1. a kind of greenhouse environment intelligent control method based on extreme learning machine network, which is characterized in that the method includes with
Lower preparation process：
1）The collection of sample data：According to the empirical data for certain crop growth that agricultural experts are provided, then by testing,
Crops are obtained in the practical intensity of illumination of different growth periods and deviation, the practical soil moisture and the warp of experience intensity of illumination
Test the deviation of the soil moisture, the deviation of practical soil moisture and experience soil moisture, actual air temperature and experience air themperature
Deviation, the deviation of actual air humidity and experience air humidity, practical gas concentration lwevel and experience gas concentration lwevel
Deviation；
2）The normalized of sample data：By sampled dataIt normalizes in [0,1] section and is：
Wherein,It isA sampled data after normalized,For the minimum value of r sampled data,
For the maximum value of r sampled data；The neural network model of foundation；
3）Establish single extreme learning machine network-control model：Extreme learning machine network is made of input layer, hidden layer and output layer；
4）Establish multiple limits learning machine network-control model：Multiple extreme learning machine network models are combined, are constituted more
Extreme learning machine network-control model；
5）Intelligent control is carried out to greenhouse using multiple limits learning machine network-control model：Obtained each monopole is limited
The weighting coefficient of learning machine network brings multiple limits learning machine network model into, when the input of multiple limits learning machine network model is farming
The different time of object growth and the intensity of illumination deviation data of corresponding each time, soil moisture deviation data, air themperature are inclined
The input sample data of difference data, air humidity deviation data, gas concentration lwevel deviation data as extreme learning machine network
When, it can calculate cascade control and regulation amount using the built-up pattern, fan control regulated quantity, light compensating lamp control and regulation amount, dissipate
Hot device control and regulation amount, sunshading board control and regulation amount, motor control and regulation amount of leaking informaton；To cascade control and regulation amount, fan control
Regulated quantity, light compensating lamp control and regulation amount, radiator control and regulation amount, sunshading board control and regulation amount, motor control and regulation amount of leaking informaton
After each numerical value carries out anti-normalization processing, green house control parameter can be obtained.
2. a kind of greenhouse environment intelligent control method based on extreme learning machine network according to claim 1, feature
It is, the collection of the sample data is by the intensity of illumination variation of the different time of crop growth and corresponding Each point in time
According to, soil moisture deviation data, air themperature deviation data, air humidity deviation data, gas concentration lwevel deviation data make
For the input sample data of extreme learning machine network.
3. a kind of greenhouse environment intelligent control method based on extreme learning machine network according to claim 2, feature
It is, the collection of the sample data is by the corresponding cascade control and regulation amount of each deviation data of different time, fan control
Regulated quantity, light compensating lamp control and regulation amount, radiator control and regulation amount, sunshading board control and regulation amount, motor control and regulation amount of leaking informaton
Sample output data as extreme learning machine network.
4. a kind of greenhouse environment intelligent control method based on extreme learning machine network according to claim 1, feature
It is, the normalized of the sample data, r sample data after normalized is divided into two groups：One group as each
The common initial training data set of single neural network model is respectively trained each single extreme learning machine network；Another group
As verification data collection, for verifying established neural network model.
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Application publication date: 20180724 |