Self-propelled machinery, which is a crucial component of agricultural mechanization, is essential for enhancing production. Few farming operations involve the operation of riding-type self-propelled machinery for a prolonged period of time (6–8 h/day) and in a fluctuating weather condition in field condition. To alleviate the operator's workload, an ergonomically designed self-propelled reach envelope was developed using various configuration simulator systems. To model the process and achieve the desired output for the development of simulator of self-propelled machinery, artificial neural network (ANN) technique was employed. The simulator was tested using a range of control lever positions (0–23) and engine speeds (1600 and 2000 rpm). The study investigated how these process parameters influence the oxygen consumption rate (OCR) of female farm workers and determined the optimal operating parameters corresponding to the 100% OCR of female agricultural workers. Genetic algorithm (GA) was used along with the developed ANN model to forecast the OCR of agricultural workers. The experimental mean ± SD values were found 0.79 ± 0.04 L/min and predicated ANN values were found to be 0.776 ± 0.06 L/min for OCR, respectively. It is evident that not all operating parameters have the same effect on OCR. The training, testing, validation, and overall data sets employed in the ANN model and yielded correlation coefficients (R²) of 0.50889, 0.44229, 0.39029, and 0.48323, respectively. The goodness of fit was another crucial factor in evaluating the ANN tools. The ANN model demonstrated the lowest mean-square error value that indicates higher precision and predictive power. The results satisfy the most suitable optimal parameters for the OCR of female workers operating the self-propelled machinery simulator.

中文翻译：

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利用人工神经网络和遗传算法对自航机到达包线进行建模和优化

自行式机械是农业机械化的重要组成部分，对于提高产量至关重要。很少有农业作业会在田间条件下长时间（6-8 小时/天）和波动的天气条件下操作骑乘式自走式机械。为了减轻操作员的工作量，使用各种配置模拟器系统开发了符合人体工程学设计的自走式伸展范围。为了对自走式机械模拟器的开发过程进行建模并实现所需的输出，采用了人工神经网络（ANN）技术。使用一系列控制杆位置（0-23）和发动机转速（1600 和 2000 rpm）对模拟器进行了测试。该研究调查了这些工艺参数如何影响女性农场工人的耗氧率（OCR），并确定了与女性农场工人100% OCR相对应的最佳操作参数。遗传算法（GA）与开发的 ANN 模型一起用于预测农业工人的 OCR。对于 OCR，实验平均值 ± SD 值分别为 0.79 ± 0.04 L/min，预测 ANN 值分别为 0.776 ± 0.06 L/min。显然，并非所有操作参数对 OCR 都有相同的影响。ANN 模型中使用的训练、测试、验证和整体数据集产生的相关系数 ( R ² ) 分别为 0.50889、0.44229、0.39029 和 0.48323。拟合优度是评估人工神经网络工具的另一个关键因素。ANN 模型表现出最低的均方误差值，表明更高的精度和预测能力。结果满足操作自行式机械模拟器的女工OCR最合适的最佳参数。