基于SAPSO-BP网络模型的港口潮汐实时预报

SAPSO-BP network in tidal level prediction of port

为了提高港口码头潮汐预报的精度,提出一种自适应变异的粒子群优化算法SAPSO,将SAPSO优化算法与BP神经网络结合,用以潮汐水位的实时预报。SAPSO-BP网络模型运用自适应变异的PSO算法优化BP神经网络的网络参数,克服了传统BP神经网络所具有的对初始权值阈值敏感、容易陷入局部极小值的缺点,最后选用Isabel港口的实测潮汐值数据进行潮汐水位的实时预报仿真试验,用以验证SAPSO-BP预测模型的实用性和可靠性。

In order to improve the accuracy of tidal level prediction in port and wharf, we propose a selfadapting particle swarm optimization（SAPSO） algorithm to optimize the back propagation（BP） neural network model. The model is referred to as SAPSO-BP model which employs PSO to adjust control parameters of BP network.This novel model overcomes the shortcoming of traditional BP neural network, which is sensitive to the initial weight threshold and is easy to trap in local minimum. The real-measured tidal level data of Isabel port is chosen as the test database to verify the practicability and reliability of the SAPSO-BP prediction model.