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 used to train NN. The trained NN can predict the TSSC performance very well. The trained results showed that this kind of approach can converge to better solutions much faster than the earlier reported approaches. It also overcomed the weakness of slow convergence and local minima. The PSO-NN offered a new method for TSSC performance optimization.
['Bin Peng', 'Liu Zhen-quan', 'Zhang Hongsheng', 'Li Zhang']
TSSC Performance Prediction Based on PSO-NN