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首页> 外文期刊>Applied Mathematical Modelling >A hybrid metaheuristic algorithm for identification of continuous-time Hammerstein systems
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A hybrid metaheuristic algorithm for identification of continuous-time Hammerstein systems

机译：一种鉴定连续时间Hammerstein系统的混合成群质算法

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摘要

This paper presents a new hybrid identification algorithm called the Average Multi-Verse Optimizer and Sine Cosine Algorithm for identifying the continuous-time Hammerstein system. In this paper, two modifications were employed on the conventional Multi-Verse Optimizer. Our first modification was an average design parameter updating mechanism to solve the local optima issue. The second modification was the hybridization of Multi-Verse Optimizer with Sine Cosine Algorithm that will balance the exploration and exploitation processes and thus improve the poor searching capability. The proposed hybrid method was used for identifying the parameters of linear and nonlinear subsystems in the Hammerstein model using the given input and output data. A continuous-time linear subsystem was considered in this study, while there were a few methods that utilize such models. Furthermore, various nonlinear subsystems such as the quadratic and hyperbolic functions had been used in those experiments. The efficiency of the novel technique is illustrated using a numerical example and two real-world applications, which are a twin rotor system and a flexible manipulator system. The numerical and experimental results analysis were observed with respect to the convergence curve of the fitness function, the parameter deviation index, time-domain and frequency-domain responses of the identified model, and the Wilcoxon's rank test. The results showed that the proposed method was efficient in identifying both the Hammerstein model subsystems in terms of the quadratic output estimation error and parameter deviation index. The proposed hybrid method also achieved better performance in modeling of the twin-rotor system as well as the flexible manipulator system and provided better solutions compared to other optimization methods including Particle Swarm Optimizer, Grey Wolf Optimizer, Multi-Verse Optimizer and Sine Cosine Algorithm.
机译：本文介绍了一种新的混合识别算法，称为平均多韵优化器和正弦余弦算法，用于识别连续时间Hammerstein系统。在本文中，在传统的多节透析器上采用了两种修改。我们的第一个修改是解决本地Optima问题的平均设计参数更新机制。第二种修改是具有正弦余弦算法的多韵优化器的杂交，这将平衡勘探和开发过程，从而提高搜索能力差。所提出的混合方法用于使用给定的输入和输出数据识别HammerseIn模型中线性和非线性子系统的参数。在本研究中考虑了连续时间线性子系统，而有几种使用这种模型的方法。此外，这些实验中使用了各种非线性子系统，例如二次和双曲函数。使用数值示例和两个实际应用示出了新技术的效率，其是双转子系统和柔性机械手系统。相对于健身功能的收敛曲线，识别模型的参数偏差指数，时域和频域响应以及Wilcoxon的等级测试，观察到数值和实验结果分析。结果表明，该方法在识别二次输出估计误差和参数偏差指数方面识别Hammerstein模型子系统的有效。所提出的混合方法还实现了对双转子系统的建模以及柔性机械手系统的建模性能，并且与其他优化方法相比提供了更好的解决方案，包括粒子群优化器，灰狼优化器，多韵优化器和正弦余弦算法。

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