Rapid prediction method for facility agriculture environment factor

Abstract

The invention discloses a rapid prediction method for a facility agriculture environment factor. The rapid prediction method comprises the following steps of: selecting a training sample according to historical sample data, generating a training set and a test set, and carrying out normalization processing on training sample data in the training set and the test set; establishing a single-hidden layer extreme learning machine prediction model to obtain trained parameters; testing by virtue of the single-hidden layer extreme learning machine prediction model, and evaluating the predication value of the test set according to the obtained trained parameters; carrying out error calculation on the predicated value and the actually-measured value of the test set, and evaluating the generalization ability of the single-hidden layer extreme learning machine prediction model. According to the rapid prediction method disclosed by the invention, the defect that the traditional mechanism modelling calculation is complex and even if difficult to realize is overcome, the training speed is fast, the globally optimal solution can be acquired, the defects of low training speed, liability to fall into a local minimum point, and the like in the prior art are solved, and the training speed is greatly increased on the basis of keeping the model accuracy.

Description

technical field [0001] The invention discloses a method for rapidly predicting environmental factors of facility agriculture, in particular to a method for rapidly predicting meteorological factors of facility agriculture based on a single hidden layer model, and relates to the field of forecasting environment factors of facility agriculture. Background technique [0002] Facility agriculture is a comprehensive application of engineering equipment technology, biotechnology and environmental technology. According to the environmental conditions required for crop growth and development, it provides a relatively controllable or even the most suitable environment such as temperature and humidity, and to a certain extent gets rid of the dependence on the natural environment. productive agriculture. To achieve a high level of facility agricultural production and optimize facility biological environment control, it is necessary to predict the environmental factors of facility agric...

AI Technical Summary

Problems solved by technology

However, due to the inherent shortcomings of typical neural networks, such as slow training speed, easy to fall into local minimum points, and sensitive learning rate selection, its application in the prediction of environmental factors in facility agriculture is limited.

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