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In this paper, a particle swarm optimization (PSO)
algorithm is introduced to determine learning parameters
required for the backpropagation (BP) learning algorithm, which
is used for training of a feed-forward neural network (FFNN).
PSO algorithm utilized within the paper works slightly different
compared to conventional PSO (CPSO) algorithm in such a way
that each particle adjusts its position based on the best midposition
of all particles and its group’s previous best. The major
reason of such a change is to enhance the performance of CPSO
algorithm, which is explained in detail in the study suggested by
Tamer, S and et.al. To test the proposed method, a FFNN with
three layers is designed for function interpolation. Learning
parameters of the designed NN are determined by both
conventional error and trial method and the proposed method.
Afterwards, using these two groups of learning parameters, the
NN is trained and tested under the same conditions. According to
the test results, learning parameters determined by the PSO
provide a better performance and interpolating capability for the
NN than those determined by the conventional method.
International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES
Emre Çelik
Nihat Ozturk
Adem Dalcalı