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《Proceedings of the CSU-EPSA》 2006-01
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Modeling and Simulation of Reheated Vapour Temperature Based on Neural Network Predicting Model

ZHANG Yun-hui~1,LV Zhen-zhong~1,ZHAO Yu-hong~2(1.Department of Power,Southeast University,Nanjing 210096,China;2.College of Electrical Engineering,Nanhua University,Hengyang 421001,China)  
It is very important to improve the predictive precision of reheated vapour temperature for enhancing the economy and reliability of the generating unit in power station.However,precise mathematical model is hard to build by traditional methods and model's predictive precision isn't high enough because the reheated vapour temperature is under the influence of many factors.Chaotic theory supplies a new method to solve the problem.In this article,the chaotic dynamic performance of reheated vapour temperature is revealed.Chaotic performance is used to deal with input samples and determine the structure of neural network.Neural network is combined with advanced genetic algorithm to construct a new predicting model of the reheated vapour temperature.Simulation results show that the model has higher precision and faster convergence speed,and the maximum absolute value of the relative error for the model is 0.06812%.
【CateGory Index】: TM621
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1 Wang Zhanwu;;Application of genetic algorithm in multi objective optimal path selection[J];智能城市;2016-02
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