Profitability evaluation of the power listed companies based on PSO-BP neural network model

In order to make the profitability evaluation of the listed companies more effectively, and provide a basis for decision-making, profitability evaluation model was established in this paper based on particle swarm algorithm (PSO) and the back-propagation algorithm (BP). Then the case study of profitability evaluation of power listed companies in China is proposed. The model takes advantages of the features of global search in PSO and local search in BP neural network. This model not only can find the smallest value in overall situation but also can greatly enhance the running rate. It has been proved by experiment that PSO-BP neural network model obtained a more satisfying effect in profitability evaluation of listed companies.