ISSN : 1796-203X
Volume : 4    Issue : 2    Date : February 2009

System Identification Using Optimally Designed Functional Link Networks via a Fast Orthogonal
Search Technique
Hazem M. Abbas
Page(s): 147-153
Full Text:
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In this paper, a sparse nonlinear system identification method is proposed. Functional link neural
nets (FLN) with their high orders polynomial basis functions are capable of performing complex
nonlinear mapping. However, a large number of inputs and accurate modeling will require a huge
number of basis functions that need to be explored. The Fast Orthogonal Search (FOS) introduced
by Korenberg [1] is adopted here to detect the proper model and its associated parameters. The
FOS algorithm is modified by first sorting all possible nonlinear functional expansion of the input
pattern according to their correlation with the system output. The sorted functions are divided into
equal size groups, pins, where functions with the highest correlation with the output are assigned to
the first pin. Lower correlation members go the following pin and so forth. During the identification
process, candidates in lower pins are tried first. If a solution is not found, next pins join the
candidates pool for further modeling until the identification process completes within a prespecified
accuracy. The proposed architecture is tested on noisefree and noisy nonlinear systems and
shown to find sparse models that can approximate the experimented systems with acceptable

Index Terms
neural networks, functional link networks, orthogonal search, system identification, nonlinear