prediction accuracy optimization of chaotic perturbation in the analysis model of network-oriented consumption
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ID: 230675
2014
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Abstract
As the slower rate of convergence and lower study ability in the late period of network-oriented consumption prediction
model based on neural network algorithm, this paper proposed a network analysis neural model based on chaotic
disturbance optimized particle swarm. Firstly, improve the initialization of particle swarm with chaotic disturbance
optimization strategy in order to limit the initial position and the initial speed of limited particle. Then have an optimal
operation on each individual in particle swarm with chaotic disturbance variables, so that the particles which do not enter
into iteration will jump out of the local optima area. And next, optimize the PSO algorithm inertia weight by adopting
adaptive adjustment strategy based on individual particle adaptive value. At last, combine the improved PSO algorithm
based on chaotic disturbance with neural network algorithm, thus we will construct the network-oriented consumption
analysis model. Simulation results show that the proposed network-oriented consumption analysis neural network model
based on chaotic disturbance optimized particle swarm has greatly improved in prediction accuracy and computational
speed.
| Reference Key |
li2014journalprediction
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|---|---|
| Authors | ;Dakai Li;Li Yu |
| Journal | communications in statistics: simulation and computation |
| Year | 2014 |
| DOI |
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