Design and Implementation of a Small Fish Pond Water Quality Detection Instrument Based on Improved Neural Network
To improve the accuracy and speed of preliminary analysis of water quality in small fish ponds,a water quality analysis method for small fish ponds based on BP neural network is proposed.This method first establishes water quality rating standards based on the growth status of freshwater aquaculture organisms in different water quality bodies.Then,a BP neural network improved based on LM algorithm is used for water quality rating analysis,and the similarity between different sample water quality is measured through Gaussian membership degree to reduce the impact of fuzziness and uncertainty in water quality status on water quality rating.Through simulation experiments,it has been proven that the improved BP neural network has a water quality classification accuracy of 99.3％,and the network training time is only 4.967 seconds.It has a fast training speed and good water quality classification performance for small fish ponds;Through physical experiments,it has been proven that the overall evaluation results of the water quality level of small fish ponds using this method are relatively good,which basically meets the preliminary analysis requirements of the water quality status of small fish ponds.
万方数据
维普
