Title: Research of Steel Production Consumption Forecast Based on Simulated Annealing PSO-BP Algorithm
Abstract: Against the existing problems in energy modeling and forecasting methods of BP neural networks,a particle swarm optimization(PSO) based on simulated annealing(SA) to optimize BP neural network is proposed.Combining with advantages of the simulated annealing algorithm and particle swarm algorithm,the weights and threshold of BP neural network are optimized.Then the optimized network is trained,and established a model for predicting Guangzhou iron and steel group energy consumption.Comparing with BP neural network and least square method,the results of the simulation show that the hybrid algorithm can enhance the generalization ability of neural networks and has the advantage of smaller error,higher precision and better tracking.
Publication Year: 2012
Publication Date: 2012-01-01
Language: en
Type: article
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