Prediction Of GDP Based On Particle Swarm Optimization And Neural Network

Economic globalization is an important feature of the development of the world economy.It not only helps to accelerate the establishment of the domestic market economic system,but also promotes the growth and development of domestic enterprises.But it has brought us more challenges.How to accurately grasp the short-term trend of the economic development has attracted more people's attention.Making accurate prediction of the future development of things,can provide a reliable basis for economic decision-making and ultimately achieve better results.So it is very important to accurately predict the GDP to provide the decision support for the government to adjust the economic structure and the macro economy.Firstly,a swarm intelligence optimization algorithm particle swarm optimization algorithm is introduced.The algorithm is based on the simulation of migration.Its convergence rate is fast,easily realize and only a few parameters need to be adjusted.Unfortunately,it is easy to fall into local optimum.Put forward to join the acceleration constants C1,C2 on the standard particle swarm algorithm was improved,then use the standard particle swarm algorithm and improved particle swarm algorithm respectively to Ackley,Rastrigin,Rosenbrock and Schaffer function to optimization test.The experimental results show that the improved algorithm can effectively improve the global optimization ability,which is suitable for solving optimization problems.Secondly,according to static feedforward BP neural network does not have memory problems proposed by dynamic,I propose a dynamic and local memory function of Elman neural network to establish a predictive model of power.At the same time,the improved particle swarm optimization algorithm is used to optimize the weight and threshold of Elman neural network,which improves the training efficiency of the network.Finally,taking Anhui Province as the research object,the influence factors of GDP are analyzed in detail.Based on this,a particle swarm optimization algorithm is established to optimize the GDP prediction model of Elman neural network.PSO optimization of the Elman neural network model,the traditional Elman neural network model and BP neural network model prediction results were compared with the actual GDP data.The results show that the proposed PSO-Elman neural network model has high prediction accuracy.