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首页> 外文期刊>American journal of applied sciences >Neural Network in Modeling Malaysian Oil Palm Yield
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Neural Network in Modeling Malaysian Oil Palm Yield

机译：神经网络在马来西亚油棕产量建模中的应用

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Problem statement: Forecasting of palm oil yield lias become an important element in the management of oil palm industry for proper planning and decision making. The importance of yield forecasting has led us to explore modeling of palm oil yield for Malaysia using the most recent development of Artificial Neural Network (ANN). The main issue in yield forecasting is to predict the future value with the minimum error. Approach: Artificial neural networks are computing systems containing many interconnected nonlinear neurons, capable of extracting linear and nonlinear regularity in a given data set. It is an artificial intelligence model originally designed to replicate the human brain's learning process, a network with many elements or neurons that are connected by communications channels or connectors. The ANN can perform a particular function when certain values are assigned to the connections or weights between elements. In this study, a secondary data set from the Malaysian Palm Oil Board (MPOB) on the foliar nutrient composition, fertilizer trials and Fresh Fruit Bunch (FFB) yield were taken and analyzed. The foliar nutrient composition variables are the nitrogen N, phosphorus P, potassium K, calcium Ca and magnesium Mg concentration, while the fertilizer trials data are the N, P, K. and Mg fertilizers and are measured in kg per palm per year. The foliar composition data was presented in the form of measured values whiles the fertilizer data in ordinal levels, from zero to three. Results: Two experiments were conducted to demonstrate the implementation ANN and for both experiment, the result demonstrated that the number of hidden nodes produces an effect to the overall forecast performance of the ANN architecture. From the first experiment, it shows that the number of runs does not affect the ANN performance, but changing the momentum to learning rates, due to shows a significant improvement in the forecast result. The experimental result will be in the form of statistical analysis, the best neural network performance, the residual analysis and the effect on the learning rate on the NN performance. Conclusion: This study showed that modeling of oil palm yield using neural network requires data to be prepared or modified to satisfy the requirement of the parameters involved. This analysis yields the conclusion that only the number of hidden nodes has a significant influence on the NN performance and there is no effect resulting from the number of runs or the momentum term value on the neural network's performance.
机译：问题陈述：棕榈油单产量的预测成为油棕榈产业管理中进行适当计划和决策的重要因素。产量预测的重要性促使我们使用人工神经网络（ANN）的最新发展来探索马来西亚棕榈油产量的模型。产量预测中的主要问题是以最小的误差预测未来价值。方法：人工神经网络是包含许多互连的非线性神经元的计算系统，能够提取给定数据集中的线性和非线性规律性。它是一种人工智能模型，最初旨在复制人脑的学习过程，这是一个由许多元素或神经元通过通讯通道或连接器连接的网络。当某些值分配给元素之间的连接或权重时，ANN可以执行特定功能。在这项研究中，从马来西亚棕榈油委员会（MPOB）获得了关于叶面营养成分，肥料试验和新鲜水果束（FFB）产量的辅助数据集并进行了分析。叶面养分组成变量为氮，磷，钾，钙，镁和镁的含量，而肥料试验数据为氮，磷，钾和镁的肥料，以每年每棵棕榈的千克数计量。叶面成分数据以测量值的形式显示，而肥料数据以序数水平显示（从零到三）。结果：进行了两个实验以演示ANN的实现，并且两个实验都表明隐藏节点的数量对ANN体系结构的整体预测性能产生影响。从第一个实验开始，它表明运行次数不会影响ANN的性能，但是会改变势头，从而影响学习率，原因是预测结果显着改善。实验结果将采用统计分析，最佳神经网络性能，残差分析以及对学习率对NN性能的影响的形式。结论：这项研究表明，使用神经网络对油棕产量进行建模需要准备或修改数据，以满足相关参数的要求。该分析得出的结论是，仅隐藏节点的数量对NN性能有重要影响，而运行次数或动量项值对神经网络的性能没有影响。

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