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- W1971776847 abstract "The traditional BP neural network algorithm has some bugs such that it is easy to fall into local minimum and the slow convergence speed. Particle swarm optimization is an evolutionary computation technology based on swarm intelligence which can not guarantee global convergence. Artificial Bee Colony algorithm is a global optimum algorithm with many advantages such as simple, convenient and strong robust. In this paper, a new BP neural network based on Artificial Bee Colony algorithm and particle swarm optimization algorithm is proposed to optimize the weight and threshold value of BP neural network. After network traffic prediction experiment, we can conclude that optimized BP network traffic prediction based on PSO-ABC has high prediction accuracy and has stable prediction performance." @default.
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- W1971776847 date "2013-10-16" @default.
- W1971776847 modified "2023-10-01" @default.
- W1971776847 title "Network Traffic Prediction based on Particle Swarm BP Neural Network" @default.
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- W1971776847 doi "https://doi.org/10.4304/jnw.8.11.2685-2691" @default.
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