Abstract

This research mainly based on multilayer perceptron (MLP) neural networks technique of data mining to forecast the wheat crop yield at the district level. There are many statistical and simulation models available, but the proposed algorithm with new activation function provides promising results in a shorter time with more accuracy. Sigmoid and hyperbolic tangent activation functions are widely used in the neural network. The activation functions play an important role in the neural network learning algorithm. The main objective of the proposed work is to develop an amended MLP neural network with new activation function and revised random weights and bias values for crop yield estimation by using the different weather parameter datasets. MLP model has been tested by existing activation functions and newly created activation functions with different cases including weights and bias values. In this research study, we evaluate the result of different activation functions and recommend some new simple activation functions, named DharaSig, DharaSigm and SHBSig, to improve the performance of neural networks and accurate results. Also, three new activation functions created with little variations in the DharaSig function named DharaSig1, DharaSig2 and DharaSig3. In this research study, variable numbers of hidden layers are tested with the variable number of neurons per hidden layer for the agriculture dataset. Variable values of momentum, seed and learning rate are also used in this study. Experiments show that newly created activation functions provide better results compared to ‘sigmoid’ default neural network activation function for agriculture datasets.

中文翻译：

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基于神经网络新激活函数的小麦产量预测

摘要

这项研究主要基于数据挖掘的多层感知器（MLP）神经网络技术来预测区域一级的小麦作物产量。有许多统计和仿真模型可用，但是所提出的具有新激活函数的算法可在更短的时间内以更高的准确性提供有希望的结果。乙状和双曲正切激活函数在神经网络中广泛使用。激活函数在神经网络学习算法中起着重要作用。拟议工作的主要目的是通过使用不同的天气参数数据集，开发具有新的激活函数和修正的随机权重和偏差值的修正的MLP神经网络，以估计作物产量。MLP模型已通过现有激活函数和新创建的激活函数（包括权重和偏差值）在不同情况下进行了测试。在这项研究中，我们评估了不同激活函数的结果，并推荐了一些新的简单激活函数，分别称为DharaSig，DharaSigm和SHBSig，以提高神经网络的性能和准确的结果。另外，创建了三个新的激活函数，它们的名称DharaSig1，DharaSig2和DharaSig3几乎没有变化。在本研究中，使用农业数据集的每个隐藏层可变数量的神经元来测试可变数量的隐藏层。本研究中还使用了动量，种子和学习率的可变值。