Application of Chaotic Particle Swarm Optimization in Wavelet Neural Network
Abstract: Currently, the
method of optimizing the wavelet neural network with particle swarm plays a
certainrole in improving the convergence speed and accuracy; however, it is not
a good solution for problems of turning into local extrema and poor global
search ability. To solve these problems, this paper, based on the particle
swarm optimization, puts forward an improved method, which is introducing the
chaos mechanism into the algorithm of chaotic particle swarm optimization.
Through a series of comparative simulation experiments, it proves that applying
this algorithm to optimize the wavelet neural network can successfully solve
the problems of turning into local extrema, and improve the convergence speed
of the network, in the meantime, reduce the output error and improve the search
ability of the algorithm. In general, it helps a lot to improve the overall
performance of the wavelet neural network.
Author: Cuijie Zhao
Journal Code: jptkomputergg140121
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