基于神经网络的水产养殖水质预测模型研究

Study on Prediction Model of Aquaculture Water Quality Based on Artificial Neural Network

水产养殖池塘是一个多变量、非线性和大时延系统,很难用传统方法建立水质预测的精确模型。神经网络具有良好的非线性函数逼近能力,非常适合处理水质预测等复杂问题。利用BP神经网络模型,通过自适应的动态学习方法和模型优化,采用MATLAB神经网络工具箱建立了水产养殖水环境因子pH值预测模型。在预测模型中输入测试样本,将预测结果与实测值进行比较,平均相对误差小于1%。结果表明,所构建的基于自适应BP算法的水产养殖水质预测模型具有良好的精确性和准确性,能有效地预测养殖池塘的水质状况。

Since the aquaculture pond is a multi-variables,nonlinearity and long-time lag system,the traditional water quality prediction method could not easily establish an accurate model.Neural network has the advantage of approximating the nonlinear function,it is an ideal method for dealing with the complex problems such as prediction of water quality.A prediction model of pH based on BP neural network was established by applying a dynamic self-adaptive learning method and model optimization.This model was completed by programming with neural network toolbox of MATLAB.The results showed that the average relative error was less than 1% between the value of prediction and the measured value when the trained network was applied in prediction.It was indicated that the prediction model of aquaculture water quality based on self-adaptive BP algorithm had good accuracy and precision,and it could effectively predict the fishery water quality.