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Artificial culture aquatic product growth prediction method and system based on MapReduce and BP neural network

A BP neural network and prediction method technology, which is applied in the field of artificial aquaculture growth prediction, can solve the problems of affecting the operation efficiency of the algorithm, high calculation overhead, low learning efficiency, etc., to speed up the convergence speed, improve the prediction accuracy, and improve the learning efficiency. Effect

Active Publication Date: 2021-06-25
TIANJIN AGRICULTURE COLLEGE
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although these advantages make it widely used in various fields, its own shortcomings are gradually exposed.
The BP neural network algorithm has high computational overhead during training, which makes its learning efficiency low and the convergence speed slow. This problem is especially obvious when dealing with large-capacity data sets, which seriously affects the operating efficiency of the algorithm.

Method used

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  • Artificial culture aquatic product growth prediction method and system based on MapReduce and BP neural network
  • Artificial culture aquatic product growth prediction method and system based on MapReduce and BP neural network
  • Artificial culture aquatic product growth prediction method and system based on MapReduce and BP neural network

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Embodiment Construction

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the application with reference to the drawings in the embodiments of the application. Apparently, the described embodiments are only some, not all, embodiments of the application. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of this application.

[0033] In addition, in the exemplary embodiments, since the same reference numerals denote the same components with the same structure or the same steps of the same method, if one embodiment is exemplarily described, only the same elements as those already described will be described in other exemplary embodiments. Different structures or methods of the embodiments are described.

[0034] Throughout the specification and claims, when one element is described as being "connected" to another elemen...

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Abstract

The invention provides an artificial culture aquatic product growth prediction method and system based on an MapReduce and BP neural network A model constructed by the BP neural network fused with an MapReduce algorithm is adopted to predict growth of the artificially cultured aquatic products, the invention can be generally suitable for a large number of data samples, the fitting effect is good, and prediction precision is improved.

Description

technical field [0001] The invention relates to the technical field of neural networks, in particular to a method and system for predicting growth of artificial aquaculture aquatic products based on MapReduce and BP neural network. Background technique [0002] At this stage, intensive and high-density breeding methods are mostly used to improve the production and efficiency of farming. In the intensive aquaculture system, the breeding system adopts online water quality parameter detection technology, automatic feeding control technology and automatic regulation technology of some water quality parameters. However, there are problems such as frequent occurrence of diseases and increased management difficulty, and the demand for fine management is increasingly urgent. The growth and development of aquatic organisms is the key factor affecting their yield. Therefore, it is necessary to use advanced technologies such as neural networks to establish growth models of aquatic orga...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/02G06N3/04G06N3/08
CPCG06Q10/04G06Q50/02G06N3/084G06N3/044
Inventor 田云臣侯嘉康
Owner TIANJIN AGRICULTURE COLLEGE