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首页> 外文期刊>Arabian Journal for Science and Engineering. Section A, Sciences >Identifying the Parameters of Cole Impedance Model Using Magnitude Only and Complex Impedance Measurements: A Metaheuristic Optimization Approach
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Identifying the Parameters of Cole Impedance Model Using Magnitude Only and Complex Impedance Measurements: A Metaheuristic Optimization Approach

机译：使用幅度和复杂的阻抗测量来识别COLE阻抗模型的参数：成型优化方法

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摘要

Due to the good correlation between the physiological and pathological conditions of fruits and vegetables and their equivalent Cole impedance model parameters, an accurate and reliable technique for their identification is sought by many researchers since the introduction of the model in early 1940s. The nonlinear least squares (NLS) and its variants are examples of the conventional optimization techniques that are commonly used in literature to tackle this problem based on complex-valued impedance measurement data. However, as happens in most conventional techniques, the NLS and its variants are subject to falling local optimal solutions and prone to getting distracted by outliers. This motivated the authors to use six meta-heuristic optimization techniques to estimate the Cole impedance models' parameters on the basis of either complex impedance or magnitude-only impedance experimental measurements. Most of the meta-heuristic optimization algorithms under investigation are entirely new to this application. These algorithms include the: salp optimization algorithm (SSA), moth-flame optimizer (MFO), whale optimization algorithm (WOA), grey wolf optimizer (GWO), cuckoo search optimizer (CS) and flower pollination algorithm (FPA). The comparison with the NLS algorithm and most of the bio-inspired algorithms under investigation show greater consistency and accuracy with regard to the estimated parameters. A box plot statistical analysis is carried out to prove the effectiveness of the investigated bio-inspired algorithms.
机译：由于水果和蔬菜的生理和病理条件与其等同的COLE阻抗模型参数之间的相关性，许多研究人员在20世纪40年代初推出了许多研究人员，因此寻求准确可靠的技术。非线性最小二乘（NLS）及其变体是常规优化技术的示例，其通常用于基于复值阻抗测量数据来解决该问题的文献。然而，正如在大多数传统技术中发生的那样，NLS及其变体受到局部最佳解决方案的落下，并且容易被异常值分散注意力。这激发了作者使用六种元启发式优化技术，基于复杂阻抗或仅限幅度阻抗实验测量来估计COLE阻抗模型的参数。调查下的大多数元启发式优化算法对于本申请完全是新的。这些算法包括：SALP优化算法（SSA），飞蛾 - 火焰优化器（MFO），鲸类优化算法（WOA），灰狼优化器（GWO），杜鹃搜索优化器（CS）和花授粉算法（FPA）。与NLS算法的比较和正在研究的大多数生物启发算法表现出估计参数的更大的一致性和准确性。进行盒子绘图统计分析，以证明研究生物启发算法的有效性。

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