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Particle swarm optimization and neural networks (PSO-NN) were proposed for twin-spirals scroll compressor (TSSC) performance prediction. The method integrated evolutionary mechanism of PSO and self-learning, nonlinear approach ability of NN. The main structure parameters of TSSC were been as input variables and the main performance parameters were been as output variables in established NN. PSO was...
Particle swarm optimization and neural networks (PSO- NN) was proposed for twin-spirals scroll compressor (TSSC) performance prediction. The method integrated evolutionary mechanism of PSO and self-learning, nonlinear approach ability of NN. In established NN the input variables were main structure parameters and the output variables were main performance parameters. PSO was used to train NN. The...
An improved PSO based on the Metropolis criterion, called MPSO, is proposed and applied in long-term streamflow forecast. MPSO focuses on the inertia weight of PSO, and leads the inertia weight to adjust in accordance to the direction of global best value based on Metropolis sampler. In the case study, MPSO is employed to estimate the coefficients of multivariable linear regressive model (MLRM). MLRM...
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