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, 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.