Least-Mean-Square Training of Cluster-Weighted Modeling
碩士 === 國立臺灣大學 === 資訊工程學研究所 === 94 === 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 (...
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| Format: | Others |
| Language: | en_US |
| Published: |
2006
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| Online Access: | http://ndltd.ncl.edu.tw/handle/97249490766554815676 |
| Summary: | 碩士 === 國立臺灣大學 === 資訊工程學研究所 === 94 === 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.
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