大连海事大学学报
大連海事大學學報
대련해사대학학보
JOURNAL OF DALIAN MARITIME UNIVERSITY
2009年
3期
61-64
,共4页
自组织神经网络(SOFM)%剪切位点%卡尔曼滤波器(KF)%扩展卡尔曼滤波器(EKF)%无先导卡尔曼滤波器(UKF)
自組織神經網絡(SOFM)%剪切位點%卡爾曼濾波器(KF)%擴展卡爾曼濾波器(EKF)%無先導卡爾曼濾波器(UKF)
자조직신경망락(SOFM)%전절위점%잡이만려파기(KF)%확전잡이만려파기(EKF)%무선도잡이만려파기(UKF)
self-organizing feature maps ( SOFM)%splice sites%kalman filter ( KF)%extend Kalman filter ( EKF)%unscented Kalman filter(UKF)
为提高基因序列中剪切位点的识别率,将无先导卡尔曼滤波器(UKF)和自组织神经网络(SOFM)相结合,给出一种非线性高维数据的聚类算法.利用无先导变换(UT)参数化SOFM邻域宽度函数的均值和方差,并采用UKF进行预测,完成SOFM参数的自适应过程.该算法用于基因剪切位点的识别结果表明:较SOFM与EKF参数自适应方法,该算法识别精度较高,验证了其有效性和可行性.
為提高基因序列中剪切位點的識彆率,將無先導卡爾曼濾波器(UKF)和自組織神經網絡(SOFM)相結閤,給齣一種非線性高維數據的聚類算法.利用無先導變換(UT)參數化SOFM鄰域寬度函數的均值和方差,併採用UKF進行預測,完成SOFM參數的自適應過程.該算法用于基因剪切位點的識彆結果錶明:較SOFM與EKF參數自適應方法,該算法識彆精度較高,驗證瞭其有效性和可行性.
위제고기인서렬중전절위점적식별솔,장무선도잡이만려파기(UKF)화자조직신경망락(SOFM)상결합,급출일충비선성고유수거적취류산법.이용무선도변환(UT)삼수화SOFM린역관도함수적균치화방차,병채용UKF진행예측,완성SOFM삼수적자괄응과정.해산법용우기인전절위점적식별결과표명:교SOFM여EKF삼수자괄응방법,해산법식별정도교고,험증료기유효성화가행성.
A clustering method for large quantities of high-dimensional data which combining unscented Kalman filter (UKF) with self-organizing feature maps (SOFM) was proposed to improve the recognition accuracy of splice sites among the gene sequences. The mean and variance of width of the neighborhood function were parameterized by unscented transform (UT) and then predicted by UKF to complete adaptive process of SOFM parameters. Tests on recognizing gene splice sites show that the proposed method has higher recognition accuracy comparing with SOFM and EFK-based parameter self-adaptive methods, which verifies its validity and feasibility.
