PhD Thesis Announcement : nonlinear filters

[ces@negi.riken.go.jp]



    FTP-host: archive.cis.ohio-state.edu
    FTP-filename: /pub/neuroprose/Thesis/chng.thesis.ps.Z


Dear fellow connectionists,

the following Ph.D. thesis is now available for copying from the
neuroprose archive: (Sorry, no hardcopies available.)



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		Applications of nonlinear filters with
		the linear-in-the-parameter structure
 
			   Eng-Siong CHNG
               	Department of Electrical Engineering
                     University of Edinburgh, U.K.

                              Abstract:		


The subject of this thesis is the application of nonlinear filters,
with the linear-in-the-parameter structure, to time series prediction
and channel equalisation problems.

In particular, the Volterra and the radial basis function (RBF) 
expansion techniques are considered to implement the nonlinear filter
structures. These approaches, however, will generate filters with 
very large numbers of parameters. As large filter models require 
significant implementation complexity, they are undesirable for practical
implementations.  To reduce the size of the filter, the orthogonal least 
squares (OLS) algorithm is considered to perform  model selection.  
Simulations were conducted to study the effectiveness  of subset models 
found using this algorithm, and the results indicate that this selection 
technique is adequate for many practical applications.
The other aspect of the OLS algorithm studied is its implementation 
requirements. Although the OLS algorithm  is very efficient, the required
computational complexity is still substantial. To reduce the processing 
requirement, some fast OLS methods are examined.

Two major applications of nonlinear filters are considered in this thesis.
The first involves the use of nonlinear filters  to predict time series
which possess nonlinear dynamics.  To study the performance of the
nonlinear predictors, simulations were conducted to compare the
performance of these predictors with conventional linear predictors.
The simulation results confirm that nonlinear predictors normally perform
better than linear predictors. Within this study, the application of RBF 
predictors to time series  that exhibit  homogeneous nonstationarity is
also considered.  This type of time series possesses  the same characteristic
throughout the time sequence apart from local variations of mean and trend. 

The second application involves the use of  filters for symbol-decision 
channel equalisation. The decision function of the  optimal  symbol-decision
equaliser is first derived  to show that it is nonlinear, and that
it may be realised explicitly using  a RBF filter. Analysis is then carried
out to illustrate the difference between the optimum equaliser's performance
and that of the conventional linear equaliser. In particular, the effects of 
delay order on the equaliser's decision boundaries and bit error rate (BER)
performance are studied. The minimum mean square error (MMSE) optimisation 
criterion for training the linear equaliser is also examined  to illustrate 
the sub-optimum nature of such a criterion. To improve the linear equaliser's 
performance, a method which adapts the equaliser by minimising the BER is 
proposed. Our results indicate that the linear equalisers 
performance is normally improved by using the minimum BER criterion.
The decision feedback equaliser (DFE) is also examined. We propose a
transformation using the feedback inputs to change  the DFE problem
to a feedforward equaliser problem. This unifies the treatment of the
equaliser structures with and without decision feedback.

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Criticism, comments and suggestions are welcome.
Merry Christmas everyone!

Eng Siong

- --------------------------------------------------------------------------
 Eng Siong CHNG                          Lab. for ABS, 
					 Frontier Research Programme,
					 RIKEN,
 email : ces at negi.riken.go.jp		 2-1 Hirosawa, Wako-Shi,
 					 Saitama 351-01,
					 JAPAN.
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    RETRIEVAL INSTRUCTIONS: 

    FTP-host: archive.cis.ohio-state.edu
    FTP-filename: /pub/neuroprose/Thesis/chng.thesis.ps.Z

    File size : 1715073 bytes
    Number of pages : 165 pages

unix> ftp archive.cis.ohio-state.edu
Connected to archive.cis.ohio-state.edu.
220 archive.cis.ohio-state.edu FTP server ready.
Name: anonymous
331 Guest login ok, send ident as password.
Password:neuron
230 Guest login ok, access restrictions apply.
ftp> binary
200 Type set to I.
ftp> cd pub/neuroprose/Thesis
250 CWD command successful.
ftp> get chng.thesis.ps.Z
200 PORT command successful.
150 Opening BINARY mode data connection for chng.thesis.ps.Z
226 Transfer complete.
ftp> quit
221 Goodbye.


unix> uncompress chng.thesis.ps.Z
unix> lpr chng.thesis.ps  (postscript printer)


Contact me if there are any problems with retrieval and or printing. 




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