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Research on the Improved Back Propagation Neural Network for the Aquaculture Water Quality Prediction

Ding Jin-ting and Zang Ze-lin
School of Information & Electrical Engineering, Zhejiang University City College, Hangzhou, China
Abstract—In order to overcome the slow convergence speed, easy to fall into the shock and generalization ability is not strong and so on of the traditional BP neural network, the adaptive variable step size BP neural network learning algorithm based on the fuzzy method has been developed, which can reduce the learning time of BP neural network, improve the convergence efficiency and network stability. According to the water quality monitoring data from one of Penaeus vannamei breeding base in Hangzhou, a mathematical model of multi-layer feed forward network was established to predict and evaluate the quality of the aquaculture water. The topology of the model is 40-14-4, that is, the temperature (T), pH value, Dissolved Oxygen (DO), and the Redox Potential (ORP) are the input variables in n=10 consecutive time units, the number of hidden layer nodes is 14, and the output layer is 4. The results show that the improved BP neural network based on fuzzy method has the characteristics of fast convergence, high accuracy and good stability. It provides a new method for the prediction and evaluation of water quality in aquaculture.
 
Index Terms—artificial neural network, BP network, water quality prediction, adaptive variable step size algorithm

Cite: Ding Jin-ting and Zang Ze-lin, "Research on the Improved Back Propagation Neural Network for the Aquaculture Water Quality Prediction," Journal of Advanced Agricultural Technologies, Vol. 3, No. 4, pp. 270-275, December 2016. Doi: 10.18178/joaat.3.4.270-275
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