进化算法
粒子群优化
计算机科学
风速计
算法
差异进化
遗传算法
进化策略
功能(生物学)
控制理论(社会学)
人工智能
机器学习
风速
气象学
物理
生物
进化生物学
控制(管理)
作者
J. Babitha Thangamalar,A. Abudhahir
出处
期刊:Circuit World
[Emerald (MCB UP)]
日期:2021-08-16
卷期号:49 (2): 113-124
标识
DOI:10.1108/cw-09-2020-0251
摘要
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.
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