Water demand prediction method based on optimized combination neural network

A neural network and prediction method technology, applied in neural learning methods, biological neural network models, gene models, etc., to achieve good global search capabilities and eliminate information overlap

Inactive Publication Date: 2020-06-16
HEBEI UNIV OF ENG
View PDF5 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The MIV algorithm can effectively eliminate the information overlap of influencing factors and screen out better indicators; the MEA algorithm has good global search capabilities and can solve the problem of random selection of a simple artificial neural network subject to initial weights and thresholds. This application uses The MEA algorithm combined with the nonlinearity, high dimensionality, extensive interconnectivity and self-adaptability of the neural network, established a water demand prediction model adapted to the water demand characteristics of walnut crops

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Water demand prediction method based on optimized combination neural network
  • Water demand prediction method based on optimized combination neural network
  • Water demand prediction method based on optimized combination neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0036] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in further detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0037] This application takes the walnut crop as an example. figure 1 The change law of walnut water demand from July 1 to July 31 in 2015 and 2016 is shown. According to this figure, we can easily see that the change of water demand for walnuts in the first ten days is relatively stable. It starts to rise from the tenth day, and the water demand for walnuts increases. Regular changes.

[0038] 1. Walnut water demand forecasting process

[0039] Select five influencing factors, average air pressure P (hPa), average temperature T (°C), wind speed F (m / s), sunshine hours S (h), and relative humidity RH (%), and each influence factor is obtained through the MIV algorithm. The average impact value of the factors is sorted ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a water demand prediction method for an Elman neural network based on optimization combination, and the method comprises the steps: carrying out the screening of input variables based on an MIV (mean impact value) algorithm, and optimizing the weight and threshold of the Elman neural network through an MEA (thought evolution algorithm). The MIV algorithm can effectively eliminate information overlapping of influence factors and screen out better indexes; the MEA algorithm has good global search capability, and the problem that a pure artificial neural network is limitedby random selection of an initial weight and a threshold can be solved. Therefore, the method is higher in accuracy and better in prediction effect, can effectively predict and forecast the water demand of the crops, guarantees timely and reasonable adjustment of an irrigation system, and has a certain application value in prediction of the water demand of the crops.

Description

technical field [0001] The present invention relates to a kind of forecasting method, based on mean influence value (MIV, Mean Impact Value) algorithm, variable is screened, utilizes thinking evolution algorithm (MEA, Mind Evolutionary Algorithm) to optimize the weight value and threshold value of Elman neural network, and the water requirement of crops Forecasting belongs to the field of water resource management and communication information technology. Background technique [0002] my country is a country that is very short of water resources, and it is also a large agricultural country, and agricultural water consumption accounts for the vast majority of total water consumption. And walnut ranks first in the world's four major dry fruits. In recent years, the scale of walnut planting area and total output in my country have increased rapidly, and the cultivated area is extremely broad. As a main growing crop, if the water demand of walnut in important stages can be accu...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/02G06Q50/02G06N3/08G06N3/12
CPCG06Q30/0202G06Q50/02G06N3/08G06N3/126
Inventor 刘心邓皓李文竹
Owner HEBEI UNIV OF ENG
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap