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 文章首先討論了怎樣設計字詞碼本結構,使之能夠滿足靈活多樣的輸入方式,繼而設計了一種動態(tài)自學習語言模型,重點分析了數據平滑算法在語言模型中的應用與改進,最后通過一個輸入法示例程序,對改進前后不同情況下的輸入效果進行了測試。
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 該算法將imm算法應用于系統狀態(tài)和模型概率同時擴維的系統,能夠實時計算模型概率平滑值,為實時判斷系統模式切換提供依據,并彌補了chen算法的任意步固定滯后平滑算法的理論缺陷。
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機動目標跟蹤中的應用;利用模型集合序貫似然比檢驗,提出了一種綜合利用模型后驗概率和預測概率實現模型集合自適應的綜合格自適應多模型算法,仿真實驗表明算法有效改善了動態(tài)跟蹤精度和穩(wěn)態(tài)跟蹤性能。