The rotating disk is a basic machine part that is u sed widely in industry. The motion equation is transformed into the dynamic equa tion in real modal space. The personating intelligent integration is introduced to ...The rotating disk is a basic machine part that is u sed widely in industry. The motion equation is transformed into the dynamic equa tion in real modal space. The personating intelligent integration is introduced to improve the existing control method. These modes that affect the transverse vibration mainly are included to simulate the vibration of rotating disk, and two methods are applied separately on condition that the sensor and the ac tuator are collocated and non collocated. The results obtained by all sided si mulations show that the new method can obtain better control effect, especially when the sensor and the actuator are non collocated.展开更多
目前,各种群智能优化算法涌现且各有特色、性能各异、普适性不强、在开采沉陷领域应用较少,更为重要的是,众多学者面对该类算法,无法有效选择最优算法进行开采沉陷研究。常见的群智能优化算法中狮群算法(Lion Swarm Optimization,LSO)...目前,各种群智能优化算法涌现且各有特色、性能各异、普适性不强、在开采沉陷领域应用较少,更为重要的是,众多学者面对该类算法,无法有效选择最优算法进行开采沉陷研究。常见的群智能优化算法中狮群算法(Lion Swarm Optimization,LSO)、蝙蝠算法(Bat Algorithm,BA)、人工鱼群算法(Artificial Fish School Algorithm,AFSA)具有不同的特征,且在概率积分参数反演中鲜有应用。为此,将上述3种群智能优化算法引入概率积分参数反演中,并从抗随机误差性能、抗粗差干扰性能、观测点缺失的抗干扰性能、参数波动性和全局搜索性能等几个角度对群智能优化算法进行研究分析。模拟试验及工程实例分析表明,上述3种群智能优化算法均满足应用精度要求。LSO算法在抗随机误差干扰影响、观测点缺失的抗干扰能力方面以及参数结果总体波动性方面相对于BA、AFSA算法有一定优势;BA算法在抗粗差干扰能力方面优于LSO、AFSA算法;在全局搜索性能方面,随着反演参数解空间范围扩大为原来的2倍后,用AFSA算法反演概率积分参数的精度优于LSO、BA算法。通过详细比较分析,总结了上述3种算法在开采沉陷中的性能表现,可为有效选择合适的群智能优化算法进行概率积分参数反演提供参考。展开更多
文摘The rotating disk is a basic machine part that is u sed widely in industry. The motion equation is transformed into the dynamic equa tion in real modal space. The personating intelligent integration is introduced to improve the existing control method. These modes that affect the transverse vibration mainly are included to simulate the vibration of rotating disk, and two methods are applied separately on condition that the sensor and the ac tuator are collocated and non collocated. The results obtained by all sided si mulations show that the new method can obtain better control effect, especially when the sensor and the actuator are non collocated.
文摘目前,各种群智能优化算法涌现且各有特色、性能各异、普适性不强、在开采沉陷领域应用较少,更为重要的是,众多学者面对该类算法,无法有效选择最优算法进行开采沉陷研究。常见的群智能优化算法中狮群算法(Lion Swarm Optimization,LSO)、蝙蝠算法(Bat Algorithm,BA)、人工鱼群算法(Artificial Fish School Algorithm,AFSA)具有不同的特征,且在概率积分参数反演中鲜有应用。为此,将上述3种群智能优化算法引入概率积分参数反演中,并从抗随机误差性能、抗粗差干扰性能、观测点缺失的抗干扰性能、参数波动性和全局搜索性能等几个角度对群智能优化算法进行研究分析。模拟试验及工程实例分析表明,上述3种群智能优化算法均满足应用精度要求。LSO算法在抗随机误差干扰影响、观测点缺失的抗干扰能力方面以及参数结果总体波动性方面相对于BA、AFSA算法有一定优势;BA算法在抗粗差干扰能力方面优于LSO、AFSA算法;在全局搜索性能方面,随着反演参数解空间范围扩大为原来的2倍后,用AFSA算法反演概率积分参数的精度优于LSO、BA算法。通过详细比较分析,总结了上述3种算法在开采沉陷中的性能表现,可为有效选择合适的群智能优化算法进行概率积分参数反演提供参考。
