The paper discusses how to design the chinese character and word code to meet the various input modes at first , then designs a dynamic self - study language model , and analyses the data smoothing algorithm in the language model 文章首先讨论了怎样设计字词码本结构,使之能够满足灵活多样的输入方式,继而设计了一种动态自学习语言模型,重点分析了数据平滑算法在语言模型中的应用与改进,最后通过一个输入法示例程序,对改进前后不同情况下的输入效果进行了测试。
After reviewing several smoothing algorithms for hybrid estimation , we presented a sub optimal approach to the d step fixed - lag smoothing problem for markovian switching system by applying the basic imm structure to the system with augmented system state and mode probability . the new fixed - lag smoothing 该算法将imm算法应用于系统状态和模型概率同时扩维的系统,能够实时计算模型概率平滑值,为实时判断系统模式切换提供依据,并弥补了chen算法的任意步固定滞后平滑算法的理论缺陷。
Applications of multiple - model smoothing algorithms for maneuvering target tracking are studied via simulation , some important conclusions are obtained . based on model - set sequential likelihood ratio , an enhanced agimm , in which model - set adaptation is implemented by jointly utilizing model posterior probability and predication probability , is proposed , simulation results indicate that improvements of both dynamic and steady state tracking performance are achieved with the enhanced algorithm 仿真研究了多模型平滑算法在机动目标跟踪中的应用;利用模型集合序贯似然比检验,提出了一种综合利用模型后验概率和预测概率实现模型集合自适应的综合格自适应多模型算法,仿真实验表明算法有效改善了动态跟踪精度和稳态跟踪性能。