细菌觅食优化算法在光散射法颗粒粒度反演中的应用

发布时间：2018-01-24 13:22

本文关键词： 光散射法细菌觅食优化算法颗粒粒度反演　出处：《光学技术》2017年05期 　论文类型：期刊论文

【摘要】：针对函数约束算法中传统的智能算法反演时存在鲁棒性差和易陷入局部最优的缺点,提出了将正则化理论与细菌觅食优化算法相结合应用在颗粒粒度的测量中。引入Tikhonov平滑泛函来构建算法的目标函数,采用L曲线法确定正则化参数;再利用细菌觅食优化算法通过趋向、聚群、复制和迁徙等四种智能行为,迭代计算来搜寻函数的最优解。实验仿真结果表明:利用细菌觅食优化算法实现了在不同程度的随机噪声下的服从J-SB分布的单峰分布的均匀球形颗粒粒度分布反演,其反演结果更稳定,反演精度高,对于实现稳定、快速、准确的颗粒粒度在线测量具有重要的意义。
[Abstract]:In view of the shortcomings of poor robustness and easy to fall into local optimum in the inversion of traditional intelligent algorithm in the function constraint algorithm. The regularization theory and the bacterial foraging optimization algorithm are combined in the particle size measurement. The Tikhonov smoothing functional is introduced to construct the objective function of the algorithm. The regularization parameters are determined by L curve method. Then we use the bacterial foraging optimization algorithm through the trend, clustering, replication and migration of four intelligent behaviors. The iterative calculation is used to search the optimal solution of the function. The experimental results show that:. The optimization algorithm of bacterial foraging is used to realize the inversion of uniform spherical particle size distribution from the single peak distribution of J-SB distribution under different degrees of random noise. The inversion results are more stable and the inversion accuracy is high. It is of great significance to realize stable, fast and accurate online measurement of particle size.
【作者单位】：中国计量大学计量测试工程学院;
【基金】：国家自然科学基金(51476154,51404223) 浙江省自然科学基金(LQ14E060003,LY13E060006) 能源清洁利用国家重点实验室开放基金
【分类号】：O436.2;TP18
【正文快照】： 0引言光散射法是指光束入射到含有被测颗粒的区域时,会被颗粒吸收和产生散射,散射光强与颗粒力度有关[1]。在对颗粒进行测量的多种方法中,光散射法有其独特的优势,已被广泛使用[2]。在对颗粒粒度测量中,反演算法尤为重要。反演算法分为函数约束算法和无函数约束算法两种[3],其

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