| Summary: | 碩士 === 國立中山大學 === 機械工程學系研究所 === 89 === Neural networks can be looked as a kind of intruments which is
able to learn. For making the fruitful results of neural networks'
learning possess parctical applied value, the thesis makes use of
linear regression technics to strengthen the extended capability of
RBF neural networks.
The thesis researches the training methods of RBF neural networks,
and retains the frame of OLS(orthogonal least square) learning rules
which is published by Chen and Billings in 1992. Besides, aiming at
the RBF's characteristics, the thesis brings up improved learning rules
in first and second phases, and uses " early stop" to be the condition
of training ceasing.
To sum up, chiefly the thesis applies some technics of statistic
linear regression to strenthen the extended capability of RBF, and
using different methods to do computer simulation in different noise
situations.
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