关键词: 粒子群算法, BP神经网络, 定额编制, 优化模型, 水利工程

Abstract: Slow convergence, low accuracy and poor stability are the shortcomings when using BP neural network model to prepare water conservancy quota. It is proposed that the initial weight threshold of BP neural network should be optimized by particle swarm optimization, which combines the global search ability of particle swarm and the local exploration ability of BP network. During the model programming using MATLAB algorithm, key parameters are determined first as well as the pre-processing of raw data; then the initial connection value of BP neural network is optimized using standardized particle swarm optimization algorithm; lastly, the optimized value is introduced into the model for training and predicting. Each model was run fifty times continuously. The results show that the dual output prediction accuracy of the BP model are 11.13% and 8.41%, 10 times of which fail to reach the expected accuracy; the dual output prediction accuracy of PSO-BP model are 5.65% and 5.44% with the expected accuracy achieved meeting the objectives. It is concluded that PSO-BP model performs better than BP neural network in prediction accuracy and stability, and thus more suitable for guiding the preparation of water quotas.

Key words: water conservancy project, particle swarm optimization, BP neural network, quota preparation, optimization model