The application of BP Neural Network based on improved PSO in BF temperature forecast

The BP network has the disadvantages such as low learning efficiency, low speed of convergence, easily falling into the local minimum state, poor ability to adapt, ect. For PSO algorithm, it is fast for convergence, especially at the initial stage, simple for the computing, and is easy to implement. Compared with the genetic algorithms, it does have not the complex operations of hybrid codecs, mutation, so it is a good optimization algorithm. However, PSO algorithm also has some shortcomings it is more and more slow for convergence rate at the late evolution of the algorithm. In this paper, a new BP Neural Network based on improved Particle Swarm Optimization (PSO) is proposed. The convergence speed of this algorithm and the capacity of searching global extremum is increased through adjusting the adaptive capacity of learning factor. The simulation results illustrate that the improved PSO is superior to the standard BP algorithm and particle swarm optimization.