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智能优化算法及其在打浆优化中的应用
引用本文:侯勇严,郭文强,党宏社.智能优化算法及其在打浆优化中的应用[J].计算机测量与控制,2008,16(11):1603-1605.
作者姓名:侯勇严  郭文强  党宏社
作者单位:陕西科技大学,电气与信息工程学院,陕西,西安,710021
基金项目:陕西省教育厅专项研究计划项目,陕西科技大学自然科学基金资助项目
摘    要:提出了一种遗传算法与神经网络技术相结合的智能优化算法,实现打浆过程的优化控制;首先针对打浆过程中系统的非线性、工艺参数间关系的不确定性,对打浆过程采用神经网络建立输入与输出之间的非线性模型,再利用遗传算法对控制参数寻求决策变量优化求解;通过改进遗传算法的交叉、变异算子等,使算法在优化过程中能有效地保持种群的多样性,防止种群过早收敛、局部收敛的现象,以实现打浆全局最优控制;实践表明,该智能优化算法,在满足打浆前后性能指标的同时明显降低了打浆能耗,是解决过程控制优化问题的可行之路。

关 键 词:智能优化  神经网络  遗传算法  打浆

Intelligence Optimization Algorithm and Its Application in Beating Optimization
Hou Yongyan,Guo Wenqiang,Dang Hongshe.Intelligence Optimization Algorithm and Its Application in Beating Optimization[J].Computer Measurement & Control,2008,16(11):1603-1605.
Authors:Hou Yongyan  Guo Wenqiang  Dang Hongshe
Affiliation:(College of Electrical and Information Engineering,Shaanxi University of Science and Technology,Xi’an 710021,China)
Abstract:An intelligent optimization algorithm which utilized genetic algorithm and neural network is proposed to achieve the optimization control of beating process of paper-making.Firstly,aiming at the nonlinear and uncertainty among the parameters in beating process,the nonlinear model for this process is established using neural network.Then the optimization for decision-making variables of control system is found by genetic algorithm.A crossover and mutation operator in standard genetic algorithm is improved for the sake of maintaining the population diversity and avoiding premature and local convergence.This optimization algorithm optimizes the control parameters and realizes the global optimal control for beating.Practices demonstrate that this intelligent optimization algorithm decreases beating power cost significantly and at the same time,meets the requirements for beating performance.This method is promising to solve the optimization for process control.
Keywords:intelligence optimization  neural network  genetic algorithm  beating
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