Purpose

This study aims to propose optimised function-based evolutionary algorithms in this research to effectively replace the traditional electronic circuitry used in linearising constant temperature anemometer (CTA) and Microbridge mass flow sensor AWM 5000.

Design/methodology/approach

The proposed linearisation technique effectively uses the ratiometric function for the linearisation of CTA and Microbridge mass flow sensor AWM 5000. In addition, the well-known transfer relation, namely, the King’s Law is used for the linearisation of CTA and successfully implemented using LabVIEW 7.1.

Findings

Investigational results unveil that the proposed evolutionary optimised linearisation technique performs better in linearisation of both CTA and Mass flow sensors, and hence finds applications for computer-based flow measurement/control systems.

Originality/value

The evolutionary optimisation algorithms such as the real-coded genetic algorithm, particle swarm optimisation algorithm, differential evolution algorithm and covariance matrix adopted evolutionary strategy algorithm are used to determine the optimal values of the parameters present in the proposed ratiometric function. The performance measures, namely, the full-scale error and mean square error are used to analyse the overall performance of the proposed approach is compared to a state of art techniques available in the literature.

中文翻译：

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使用基于进化优化函数的方法对流量传感器进行线性化

目的

本研究旨在提出本研究中优化的基于函数的进化算法，以有效替代线性恒温风速计 (CTA) 和 Microbridge 质量流量传感器 AWM 5000 中使用的传统电子电路。

设计/方法/途径

所提出的线性化技术有效地使用比率函数对 CTA 和 Microbridge 质量流量传感器 AWM 5000 进行线性化。此外，众所周知的传递关系，即国王定律用于 CTA 的线性化，并使用 LabVIEW 7.1 成功实施.

发现

研究结果表明，所提出的进化优化线性化技术在 CTA 和质量流量传感器的线性化方面表现更好，因此可以应用于基于计算机的流量测量/控制系统。

原创性/价值

采用进化策略算法的进化优化算法，如实数编码遗传算法、粒子群优化算法、差分进化算法和协方差矩阵，用于确定所提出的比例函数中存在的参数的最优值。性能测量，即满量程误差和均方误差用于分析所提出方法的整体性能，并将其与文献中可用的最先进技术进行比较。