Particle swarm optimization PID neural network control method in the main steam temperature control system
BP algorithm based on the gradient descent depends on initial weight selection with slow convergence rate and easily falling into local optimum. This paper presents the PSO algorithm and BP algorithm respectively in the global and local search advantage for the neural network weights optimization,The algorithm was used for the main steam temperature control system. The control strategy improved the control performance,and had a good anti-jamming performance and strong robustness,it achieved good control effect for large delay and variable object.
component PSO algorithm BP algorithm Neural network Main steam temperature control
Liu Wei Zhou Junmin
Department of Physics and Electrionic Engineering Zhoukou Normal University Zhoukou,China
国际会议
杭州
英文
137-140
2012-03-23（万方平台首次上网日期，不代表论文的发表时间）