为了准确高效的预测船舶在波浪中的航行状态以保证人员、货物和船舶的安全,提出了一种基于灰色模型粒子群优化算法的自适应神经模糊推理系统(grey particle swarm optimization-adaptive neural-fuzzy inference system, GPSO-ANFIS).GPSO-ANFIS预测模型使用模糊C均值聚类算法对输入样本进行聚类分析,得到模糊规则数量并建立神经模糊推理系统;再使用粒子群优化算法对建立的预测系统进行优化训练,从而得到最优的预测系统模型.其中灰色模型用于横摇数据的预处理,以便削弱横摇状态中的非线性影响因素.最后通过实船"育鲲"轮的横摇数据进行仿真实验.实验结果验证了GPSO-ANFIS模型的实用性和可行性,具有较高的预测精度.并为船舶航行智能化提供了一种有价值的理论依据.%In order to predict the navigation state of ship in the wind and waves accurately, timely and efficient and ensure the safety of personnel, cargo and ship, a grey particle swarm optimization algorithm combined with adaptive neural-fuzzy inference system (grey particle swarm optimization-adaptive neural-fuzzy inference system, GPSO-ANFIS) is proposed.The fuzzy c-mean clustering is introduced to confirm the fuzzy rule number to build the fuzzy inference system, the particle swarm optimization algorithm is used to train and optimize the fuzzy system and establish an optimal forecasting system model.The grey prediction theory is introduced into the proposed prediction model to alleviate the unfavorable effects of uncertainty caused by various environmental factors and the adverse effects caused thereby on the prediction accuracy.PSO-ANFIS model which combined with grey model is proposed for the real time ship rolling motion prediction.The measurement data from scientific research and training ship Yu Kun is chosen as the test database.Simulation results have demonstrated that the proposed method can give predictions for ship rolling motion in real time with high accuracy and satisfactory stability.And this proposed approach provides a valuable theoretical basis for the intelligent navigation of the ship.
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