基于混沌理论和改进极限学习机的船舶升沉预报

船舶升沉运动预报是主动升沉补偿系统中的重要组成部分.为了满足船舶升沉运动预测的实时性和准确性要求,本文提出了一种混沌理论与增强搜索极限学习机相结合的混合方法(CES-ELM).在混沌动力系统相空间重构的基础上,采用基于误差最小化的方法生成ELM隐藏节点并不断更新权值;利用优化后的模型参数建立船舶运动预测模型.不同海况下的仿真结果表明,该方法的预测平均绝对百分误差小于10％,与传统的ELM和LSSVM模型相比,该模型能有效提高预测精度和鲁棒性.

Prediction of Ship Heaving Motion Based on Chaos Theory and Improved Extreme Learning Machine

Ship heaving motion forecasting is an important aspect of the active heave compensation system. To satisfy the real-time characteristics of ship heave motion prediction with high accuracy, a hybrid method that combines chaos theory and extreme learning machine with enhancing search mod?el parameters (CES-ELM) was proposed in this paper. The error-minimization-based method was used to grow ELM's hidden nodes and unceasingly update weights based on phase space reconstruc?tion of the chaotic dynamical system. Optimized model parameters were used to establish the ship mo?tion forecasting model. The simulation results in different sea conditions indicate that the prediction mean-absolute-percentage error of the proposed method is less than 10％, and that this model can ef?fectively improve the forecast accuracy and robustness in comparison with the traditional ELM and LSSVM.