摘要
针对在重介选煤密度控制系统中运用的模糊神经网络控制器的控制参数多,收敛速度慢的问题,提出了运用量子遗传算法对其进行优化的方法。运用量子力学中的态叠加,用量子位进行编码表示染色体,在原有遗传算法的基础上,增加了种群的多样性,提高了收敛速度。试验结果表明,与传统的遗传算法优化的控制器相比较,量子遗传算法优化模糊控制器不论在控制精度上,还是在稳定性上,都有良好的效果。
In view of massive parameters and slow convergence velocity of the fuzzy neural network controller applied to the density control system for heavy medium coal separation, a quantum genetic algorithm was proposed to conduct the optimization. In the method, the state superposition of quantum mechanics was applied, and chromosomes was encoded by quantum bits, thus the number of the population and the convergence velocity increased based on original genetic algorithm. Test results showed that the fuzzy controller optimized by quantum genetic algorithm was characterized by better controller optimized by conventional genetic algorithm. control precision and stability contrasted with the
出处
《矿山机械》
北大核心
2013年第4期107-110,共4页
Mining & Processing Equipment
基金
安徽高校省级自然科学研究项目(KJ2012Z343)
淮北师范大学青年科研项目(2013XQZ16)
关键词
重介选煤
量子遗传算法
态叠加
模糊神经网络
heavy medium coal separation
quantum genetic algorithm
state superposition
fuzzy neural network