Ocean sensor data prediction has become a promising means for smart ocean monitoring. In alternative solutions, deep neural networks (DNNs) are considered as a good choice. The determination of activation functions in DNNs has a significant effect on training speed and nonlinear approximation. In this paper, the effect of activation functions on a deep computing model called deep belief echo-state network (DBEN) is studied in the scenario of ocean time series prediction. Here, different forms, including hyperbolic tangent, rectified linear unit, exponential linear unit, swish, softplus and their variants, are considered. The purpose is to investigate, from the perspectives of accuracy and training efficiency, whether certain activation function in DBEN is completely universal for the different tasks of ocean sensor data processing or not. On a great deal of real-world ocean time series of different characteristics, the results show that the selection of activation functions in DBEN is task-related. Specially, these newly introduced activation functions are more beneficial to the accurate predictions for conventional and chemical data sets compared with sigmoid benchmark. The statistical analysis further verifies this finding.

海洋传感器数据预测已成为有希望的智能海洋监测手段。在替代解决方案中，深度神经网络（DNN）被认为是一个不错的选择。DNN中激活函数的确定对训练速度和非线性逼近有重要影响。在海洋时间序列预测的情况下，研究了激活函数对称为深度信念回波状态网络（DBEN）的深度计算模型的影响。在这里，考虑了不同的形式，包括双曲正切，整流线性单位，指数线性单位，swish，softplus及其变体。目的是从准确性和培训效率的角度研究DBEN中的某些激活功能对于海洋传感器数据处理的不同任务是否完全通用。在大量具有不同特征的现实世界海洋时间序列上，结果表明，DBEN中激活函数的选择与任务有关。特别是，与S型基准相比，这些新引入的激活函数对常规和化学数据集的准确预测更有利。统计分析进一步验证了这一发现。