By combining block-wise recursive PLS with finite memory method, a new adaptive algorithm was proposed to build adaptive soft-sensor.
针对基于批量数据的传统偏最小二乘（PLS）模型无法随生产过程的变化而更新的问题，提出基于块式递推PLS的限定记忆法。
Kanerva's algorithm was an elegant method to store a finite number of data points in a very immense potential memory space.
卡内尔瓦的算法是一种将有限数量的数据点储存进非常巨大的潜在的内存空间的绝妙方法。
Compared with the finite element method, the method proposed in this paper requires less memory, less original data to input into computer, and has higher calculating accuracy.
与有限单元法相比，本方法所需的计算机内存少。原始数据输入较简单，算例表明，计算精度高。
The computational cost and memory requirements for the scheme are nearly the same as those used by the same order finite difference method.
三维格子法所需的计算机内存及计算耗时与同阶精度的规则网格有限差分法相当。
The computational results of Three Gorges Dam show that this combination method needs far less memory capacity and saves much more time than alone Finite element method with small element sizes.
经三峡大坝的计算表明，这种结合方法比单纯采用有限元方法少占用计算机许多存储单元，节省大量的机时。
The computational results of Three Gorges Dam show that this combination method needs far less memory capacity and saves much more time than alone Finite element method with small element sizes.
经三峡大坝的计算表明，这种结合方法比单纯采用有限元方法少占用计算机许多存储单元，节省大量的机时。
应用推荐