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Greenhouse environment forecasting feedback method of back propagation (BP) neural network based on improvement of genetic algorithm

A technology of BP neural network and genetic algorithm, which is applied in the prediction field of greenhouse environmental parameters, can solve problems such as the difficulty of nonlinear system identification, and achieve the effects of low training data requirements, optimization of BP neural network structure, and reduction of computing time.

Inactive Publication Date: 2013-05-15
BEIJING KINGPENG INT HI TECH CORP
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Problems solved by technology

But it is difficult to identify the general nonlinear system

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  • Greenhouse environment forecasting feedback method of back propagation (BP) neural network based on improvement of genetic algorithm
  • Greenhouse environment forecasting feedback method of back propagation (BP) neural network based on improvement of genetic algorithm
  • Greenhouse environment forecasting feedback method of back propagation (BP) neural network based on improvement of genetic algorithm

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[0031] The following drawings and examples describe in detail the greenhouse environment prediction feedback method based on the BP neural network improved by the genetic algorithm of the present invention.

[0032] The present invention is based on the greenhouse environment prediction feedback method of the improved BP neural network of genetic algorithm, comprises the following steps:

[0033] (1) Acquisition and preprocessing of data: Obtain the leaf temperature of the measured plant and the values ​​of five factors affecting it as training and testing data.

[0034] In the embodiment of the present invention, the method for obtaining the values ​​of 5 factors that have an impact on plant temperature is to take samples every hour at 3 sampling points in the selected greenhouse, monitor continuously for one month, and measure the temperature outside the greenhouse. , relative humidity, solar irradiance, insulation cover state, CO 2 Content of the values ​​of 5 indicators t...

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Abstract

A greenhouse environment forecasting feedback method of a back propagation (BP) neural network based on the improvement of a genetic algorithm comprises the following steps: obtaining a plurality of groups of temperatures of plant leaves to be tested and monitoring data values of other five factors which have influence on the temperatures, normalizing the data into values from -1 to +1, and classifying the normalized data into groups to serve as training and testing data for use; establishing the BP neural network which comprises an input layer, a middle layer and an output layer; using the genetic algorithm to optimize the established BP neural network; training and testing the optimized BP neural network; and forecasting the temperatures of the plant leaves according to the BP neural network which passes the test. Through the greenhouse environment forecasting feedback method of the BP neural network based on the improvement of the genetic algorithm, a model suitable for greenhouse environment parameter forecasting can be established, and target parameters in a greenhouse environment can be forecasted accurately and rapidly through a computer simulation test.

Description

technical field [0001] The invention relates to a prediction method of greenhouse environment parameters, in particular to a greenhouse environment prediction feedback method based on a genetic algorithm-improved BP neural network capable of accurately and quickly predicting the temperature of plant leaves in the greenhouse. Background technique [0002] Greenhouse is a complex system with characteristics of nonlinearity, randomness, strong coupling and uncertainty. The purpose of greenhouse environment modeling is mainly to meet the needs of greenhouse system simulation, design, prediction, control (optimized control and adaptive control) and decision-making. The main methods of greenhouse environment modeling are mechanism modeling and testing methods based on physical processes. Mechanism modeling based on physical process is based on reductionism. Its thinking method is to use relatively simple principles to explain complex phenomena or structures, and use the theory of...

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Application Information

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IPC IPC(8): G01K13/00G06N3/02
Inventor 周增产卜云龙田真商守海董明明吴建红
Owner BEIJING KINGPENG INT HI TECH CORP
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