NDLI: Research on the growth model of aquaculture organisms based on neural network expert system

Content Provider	IEEE Xplore Digital Library
Author	Changhui Deng Yanping Gao Jun Gu Xinying Miao Songsong Li
Copyright Year	2010
Description	Author affiliation: School of Information Engineering, Dalian Fisheries University, 116023, China (Changhui Deng; Yanping Gao; Jun Gu; Xinying Miao; Songsong Li)
Abstract	In intensive aquaculture, the growing state of aquaculture organisms is affected by many factors. Among these factors, the quality of aquaculture water has a full impact on the growth of aquaculture organisms. High quality waters will be more conducive to the aquaculture organisms' growth. It can enhance the economic value of the aquaculture, and plays an important role in the aspect of developing the cause of water aquaculture. It is mainly researched in this paper that how to establish the model about the growing status of aquaculture organisms which is based on the artificial neural network expert system. The growing state model affected by many water quality parameters is established in this project, and using artificial neural network technology can solve the bottleneck problem in the expert system. Experiment results have proved that the method is effective. When the parameters of aquaculture water changing, we can deduce that how change the growing state of the aquaculture organisms correspondingly by the model.
Starting Page	1812
Ending Page	1815
File Size	249121
Page Count	4
File Format	PDF
ISBN	9781424459582
e-ISBN	9781424459612
DOI	10.1109/ICNC.2010.5584492
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training artificial neural network expert system growing state of aquaculture organisms Biological system modeling Artificial neural networks water quality parameters Organisms Expert systems Aquaculture intensive aquaculture
Content Type	Text
Resource Type	Article

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