Least-Mean-Square Training of Cluster-Weighted Modeling
Date Issued
2006
Date
2006
Author(s)
Lin, I-Chun
DOI
en-US
Abstract
This thesis is based on Cluster-Weighted Modeling (CWM), which can be viewed as a novel uni-versal function approximator based on input-output joint density estimation. CWM is trained by Expectation-Maximization (EM) algorithm. In this thesis Least-Mean-Square (LMS) is ap-
plied to further train the model parameters and it can be viewed as a complementary training method for CWM. Due to different objective functions of EM and LMS, the local minimum should not be the same for the two objective functions. The training result of LMS learning can be used to reinitialize CWM’s model parameters which provides an approach to mitigate local minimum problems. Experiments of time-series prediction, hurricane track prediction and
Lyapunov exponents estimation are presented in this thesis.
Subjects
叢聚權重模型
最小平方法
時間序列
函數逼近
cluster-weighted modeling
least-mean-square
time series
function approximation
Type
thesis
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