Abstract Data-driven approach has been widely utilized in modeling soft sensor for predicting key quality variables in process engineering area. The soft sensor is generally a time dependent dynamical model between the input and the output. Echo state network (ESN) is a typical data-driven modeling tool, which has exhibited excellent performance in temporal data processing area. However, the memory mode in the traditional ESN lacks flexibility. It is sometimes hard to preserve sufficient input features in the states, especially for modeling long-term dependent soft sensors. To solve this problem, this paper proposes an asynchronously deep echo state network (ADESN), which is composed of a number of sub-reservoirs that are connected one by one in sequence. Additionally, time delay modules are inserted between every two adjacent layers. The ADESN scheme preserves more input history in the states. Moreover, it can realize a selective memory. The validity of the ADESN is demonstrated on modeling a number of numerical and real-life soft sensors.

中文翻译：

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使用新型深度回波状态网络对数据驱动传感器进行建模

摘要 数据驱动方法已广泛应用于软传感器建模，以预测过程工程领域的关键质量变量。软传感器通常是输入和输出之间的时间相关动力学模型。回声状态网络（ESN）是一种典型的数据驱动建模工具，在时间数据处理领域表现出优异的性能。但是，传统ESN中的存储方式缺乏灵活性。有时很难在状态中保留足够的输入特征，特别是对于建模长期依赖的软传感器。针对这一问题，本文提出了一种异步深度回波状态网络（ADESN），该网络由若干个依次连接的子水库组成。此外，每两个相邻层之间插入时间延迟模块。ADESN 方案在状态中保留了更多的输入历史。而且，它可以实现选择性记忆。ADESN 的有效性在对许多数字和现实软传感器进行建模时得到了证明。