Department of Occupational Health Engineering, Isfahan University of Medical Sciences, Iran.
Department of Biostatistics and Epidemiology, Isfahan University of Medical Sciences, Iran.
Int J Occup Saf Ergon. 2020 Sep;26(3):436-443. doi: 10.1080/10803548.2018.1435445. Epub 2018 Mar 13.
. Recently, a new method was proposed for physical work rate classification based on an adaptive neuro-fuzzy inference system (ANFIS). This study aims to present a genetic algorithm (GA)-optimized ANFIS model for a highly accurate classification of physical work rate. . Thirty healthy men participated in this study. Directly measured heart rate and oxygen consumption of the participants in the laboratory were used for training the ANFIS classifier model in MATLAB version 8.0.0 using a hybrid algorithm. A similar process was done using the GA as an optimization technique. . The accuracy, sensitivity and specificity of the ANFIS classifier model were increased successfully. The mean accuracy of the model was increased from 92.95 to 97.92%. Also, the calculated root mean square error of the model was reduced from 5.4186 to 3.1882. The maximum estimation error of the optimized ANFIS during the network testing process was ± 5%. . The GA can be effectively used for ANFIS optimization and leads to an accurate classification of physical work rate. In addition to high accuracy, simple implementation and inter-individual variability consideration are two other advantages of the presented model.
. 最近，提出了一种基于自适应神经模糊推理系统 (ANFIS) 的体力工作率分类新方法。本研究旨在提出一种遗传算法 (GA) 优化的 ANFIS 模型，以实现对体力工作率的高精度分类。. 三十名健康男性参与了这项研究。直接测量参与者在实验室中的心率和耗氧量，用于在 MATLAB 版本 8.0.0 中使用混合算法训练 ANFIS 分类器模型。使用 GA 作为优化技术进行了类似的过程。. 成功提高了 ANFIS 分类器模型的准确性、灵敏度和特异性。模型的平均准确率从 92.95%提高到 97.92%。此外，模型的计算均方根误差从 5.4186 减少到 3.1882。在网络测试过程中，优化后的 ANFIS 的最大估计误差为±5%。. GA 可有效地用于 ANFIS 优化，并导致体力工作率的准确分类。除了高精度之外，该模型的另外两个优点是简单的实现和个体间可变性的考虑。
