Abstract
Due to the use of time, machine wear degree, coal and other reasons, the original set parameters of the boiler have been unable to meet the control requirements, therefore using a large amount of data to build a real model of the power station based on the neural network, therefore, to establish a boiler combustion optimization neural network model by using of the power plant operating data. According to the shortcomings on RBF neural networks traditional training methods with slow convergence speed, easy to fall into the local minimum. Firstly, this paper Set the model to single input and single output system as the research object, optimize neural network by the particle swarm optimization algorithm. Finally, this modeling method is expanded to the multiple input multiple output system field. Use MATLAB to establish the simulation model and the simulation research, the simulation results show that improved method for combustion boiler system efficiency has been significantly improved, combustion efficiency of the entire system reached 94%, the accuracy of the system model was significantly better than ordinary neural network, system training error controls in less than 5% .We can see that the improved method is feasible and effective.
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