PREDICTING THE PERFORMANCE PARAMETERS OF CHISEL PLOW USING NEURAL NETWORK MODEL

ABSTRACT This study examines the capability of an artificial neural network (ANN) approach using a backpropagation-learning algorithm to predict performance parameters for a chisel plow at three field sites with differing soils. The draft force, effective field capacity (EFC), fuel consumption rate (FC), overall energy efficiency (OEE), and rate of plowed soil volume (SVR) were predicted at varying plowing speeds, plowing depths, soil moisture contents, soil bulk densities, soil texture indexes, and tractor powers. Collected field data was divided into a training set (for predicting the required parameters) and testing set (for model validation). For the ANN algorithm, the number of hidden layers, neurons, and transfer functions were varied to construct different ANN architectures, which were then verified using various statistical criteria, such as mean absolute error. The results showed that an ANN with one hidden layer and 15 neurons was ideal. The developed ANN model predicted the draft force, EFC, FC, OEE, and SVR of the chisel plow with a mean absolute error of 3.23 kN, 0.80 hah-1, 3.04 Lh-1, 2.78% and 1.06 m3h−1, respectively in the testing phase.

摘要 本研究针对3个土壤类型各异的试验田块，探究了采用反向传播学习算法的人工神经网络（Artificial Neural Network，ANN）对凿式犁作业性能参数的预测能力。本研究选取的预测参数包括牵引阻力、有效田间作业效率（Effective Field Capacity，EFC）、燃油消耗率（Fuel Consumption Rate，FC）、总能源效率（Overall Energy Efficiency，OEE）以及翻土体积速率（Soil Volume Rate，SVR），并设置了不同的耕作速度、耕作深度、土壤含水率、土壤容重、土壤质地指数及拖拉机功率作为变量。采集得到的田间数据被划分为训练集（用于预测目标参数）与测试集（用于模型验证）。针对该ANN算法，通过调整隐藏层数、神经元数量与传递函数构建了多种ANN架构，并借助平均绝对误差等多种统计指标对各架构进行验证。结果表明，含1个隐藏层与15个神经元的ANN架构为最优方案。所构建的ANN模型在测试阶段对凿式犁的牵引阻力、有效田间作业效率、燃油消耗率、总能源效率及翻土体积速率的预测结果，其平均绝对误差分别为3.23 kN、0.80 ha·h⁻¹、3.04 L·h⁻¹、2.78%及1.06 m³·h⁻¹。