TITLE

Research on Designs of City Temperature Control Systems Based on Particle Swarm Algorithm and Neural Network

AUTHOR(S)
JIANG Feng
PUB. DATE
September 2012
SOURCE
Journal of Convergence Information Technology;Sep2012, Vol. 7 Issue 16, p484
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
This paper has pointed that the existed systems which controls the temperature objects are large inertia, long-time delay, and time varying and nonlinear. Due to real-time features and real-time differentials, the application of the traditional PID control systems can hardly achieve good effect. In this condition, this paper proposes a main steam-temperature control system based on neural networks optimized by chaotic particle swarm, which tunes PID parameters by adopting RBF neural network online, and optimizes the initial parameters on RBF neural networks with the application of chaotic particle swarm algorithm. The said controlling algorithm not only has the adaptive ability of controlling the RBF neural network, but also has the characteristics of conventional PID cascade control, which can enhance the adaptability for the system uncertainties. The simulation results show that the control algorithm, which has better robustness, higher control efficiency, and strong resistance for interference, can be a good reference for solving the optimization problems of the boiler main steam-temperature control mentioned above.
ACCESSION #
100102782

 

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