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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 order to solve the problem of multiple control parameters and slow convergence speed of fuzzy neural network controllers used in dense medium coal density control system, a method of using quantum genetic algorithm to optimize it is proposed. Using the superposition of states in quantum mechanics, the quantum bits are used to encode the chromosomes. Based on the original genetic algorithm, the diversity of the population is increased and the convergence speed is improved. The experimental results show that, compared with the traditional genetic algorithm optimized controller, the quantum genetic algorithm optimization fuzzy controller has a good effect both in control accuracy and in stability.