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Telecommunication traffic forecasting based on BP
neural network which is optimized by particle swarm
optimization
(PSO) algorithm is presented. PSO is a novel random
optimization method based on swarm intelligence,
which has more powerful
ability of global optimization. Here, we use the
telecommunication traffic ranging fiom 1989 to 2005
in China as the sample to the
neural network, which has been trained by PSO, are
employed to illustrate the presented model. The
experimental results prove that
the proposed method optimized by PSO can quicken the
learning speed of the network and improve the
forecasting precision
compared with the conventional BP method and show
that the method is not only simple to calculate, but
also practical and effective.
neural network which is optimized by particle swarm
optimization
(PSO) algorithm is presented. PSO is a novel random
optimization method based on swarm intelligence,
which has more powerful
ability of global optimization. Here, we use the
telecommunication traffic ranging fiom 1989 to 2005
in China as the sample to the
neural network, which has been trained by PSO, are
employed to illustrate the presented model. The
experimental results prove that
the proposed method optimized by PSO can quicken the
learning speed of the network and improve the
forecasting precision
compared with the conventional BP method and show
that the method is not only simple to calculate, but
also practical and effective.