中文摘要：

传统的滑动窗策略只是简单且机械地将最远的数据移出窗口,而将最近的数据移进窗口.针对这种遗忘策略存在的缺陷,提出了过滤窗策略.过滤窗采用＂优胜劣汰＂的选择机制,将对模型贡献比较大的数据留在窗口当中.将过滤窗和最小二乘支持向量回归机相结合,提出了过滤窗最小二乘支持向量回归机.与滑动窗最小二乘支持向量回归机相比较,过滤窗最小二乘支持向量回归机具有较小的计算量,需要较短的窗口长度就能达到和滑动窗最小二乘支持向量回归机几乎相同的预测精度,而较短的窗口长度又预示着较少的计算量和较好的实时性.混沌时间序列在线建模和预测的实例表明了过滤窗最小二乘支持向量回归机的有效性和可行性.

英文摘要：

When the traditional strategy of sliding window （SW） deals with the flowing data, the data far from current position are me- chanically and briefly moved out of the window, and the nearest ones are moved into the window. To solve the shortcomings of this forgetting mechanism, the strategy of filtering window （FW） is proposed, in which adopted is the mechanism for selecting the superior and eliminating the inferior, thus resulting in the data making more contributions to the will-built model to be kept in the window. Merging the filtering window with least squares support vector regression （LSSVR） yields the filtering window based LSSVR （FW-LSSVR for short）. As opposed to traditional sliding window based LSSVR （SW-LSSVR for short）, FW-LSSVR cuts down the computational complexity, and needs smaller window size to obtain the almost same prediction accuracy, thus suggesting the less computational burden and better real time. The experimental results on classical chaotic time series demonstrate the effectiveness and feasibility of the proposed FW-LSSVR.