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Dissolved Oxygen Prediction Using RBF Network Based on Improved Conjugate Gradient Method

The concentration of dissolved oxygen is an important water quality parameter in aquaculture. In order to accurately grasp its tendency, we proposed a prediction model, namely ICG-RBF, a RBF neural network model based on improved conjugate gradient method. The model optimizes the search direction and learning rate of conjugate gradient method. We propose a hybrid conjugate gradient method which combining FR method and PRP method to optimize the search direction. And at the same time, we improve the “reward-punish” strategy to optimize the learning rate. Compared with traditional RBF neural network model, the simulation results of the ICG-RBF model has fewer iterations, faster convergence rate and higher accuracy.