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Double exponential inversionalgorithm based on differential evolution in photon correlation spectroscopy

机译:光子相关光谱中基于微分演化的双指数反演算法

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In traditional double exponential inversion algorithm of photon correlation spectroscopy (PCS), four initial parameters choice of decay linewidths and decay linewidth distribution coefficients affect the particles size inversion accuracy. In order to obtain the optimal parameters, based on differential evolution algorithm, an optimization algorithm within the global is proposed. This algorithm begin from a solution set of four parameters, iteratively calculated by operation rules of differential, variation and choice, simultaneously perform survival of the fittest according to the objective function value of each solution and gradually guide the search process approaching the optimal solution. This algorithm overcomes difficult of iterative initial value choice By new algorithm and traditional algorithm, simulation datum with noise-free and noise of 100nm-500nm bi-dispersed particles were respectively inversed. The results show new algorithm has strong tolerance of noises, when the noise levels is within 0~0.001,compared with tradition algorithm, it can reduce inversion error by 0.31% ∼ 2.52%. Therefore, new algorithm is effective double exponential inversion algorithm in particles size inversion of PCS.
机译:在传统的光子相关光谱双指数反演算法中,衰减线宽和衰减线宽分布系数的四个初始参数选择会影响粒径反演精度。为了获得最优参数,提出了一种基于差分进化算法的全局最优算法。该算法从四个参数的解集开始,通过微分,变异和选择的运算规则进行迭代计算,并根据每个解的目标函数值同时执行适量生存,并逐步引导搜索过程逼近最优解。该算法克服了迭代初始值选择的困难。通过新算法和传统算法,分别对无噪声和100nm-500nm双分散粒子的噪声模拟数据进行反演。结果表明,新算法对噪声的耐受性强,当噪声水平在0〜0.001以内时,与传统算法相比,可以将反演误差降低0.31%〜2.52%。因此,新算法是PCS粒度反演中有效的双指数反演算法。

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