Aquaculture Monitoring System Design based on BP Neural Network Algorithm

Traditional aquaculture systems have some shortcomings, such as acquired information is difficult, reasoning ability is weak, the level of intelligence is low and so on. In order to overcome these demerits, this paper elaborated a design using BP neural network in aquaculture monitoring systems, emp...

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Veröffentlicht in:	International journal of advancements in computing technology 2013-02, Vol.5 (3), p.779-779
Hauptverfasser:	Li, Xin-hui, Yu, A-long, Sun, Hong-bing, Jia, Fang-fang, Pan, Miao
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Aquaculture Back propagation Clusters Mathematical models Monitoring Neural networks Water quality
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container_title	International journal of advancements in computing technology
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creator	Li, Xin-hui Yu, A-long Sun, Hong-bing Jia, Fang-fang Pan, Miao
description	Traditional aquaculture systems have some shortcomings, such as acquired information is difficult, reasoning ability is weak, the level of intelligence is low and so on. In order to overcome these demerits, this paper elaborated a design using BP neural network in aquaculture monitoring systems, employing Fuzzy Description to quantify water quality factors in aquaculture. Characteristic of the system is introduced by system model and implementation process. The system used a slight modification of the protocol stack achieved cluster settings which set a pool manually as a cluster. The experimental data shows that the error is less than 1%. The platform overcomes subjective of relying on the expertise completely. It has high efficiency diagnosis, high practicality and flexibility.
doi_str_mv	10.4156/ijact.vol5.issue3.90
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subjects	Algorithms Aquaculture Back propagation Clusters Mathematical models Monitoring Neural networks Water quality
title	Aquaculture Monitoring System Design based on BP Neural Network Algorithm
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