Comparing Activation Functions in Modeling Shoreline Variation Using Multilayer Perceptron Neural Network

Chen, Je-Chian; Wang, Yumin

doi:10.3390/w12051281

Cited by 26 publications

(8 citation statements)

References 34 publications

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“…Every neuron performs aggregation on its weighted inputs and yields an output through an activation function. The most commonly used activation functions are the linear, the logistic and the hyperbolic tangent activation function [24]. The output value of the j-th neuron (o j ) is given by the equations as described by Dedecker et al [25]:…”

Section: Preliminaries On Ann and Model Constructionmentioning

confidence: 99%

Modelling Freshwater Eutrophication with Limited Limnological Data Using Artificial Neural Networks

Hadjisolomou

Stefanidis

Herodotou

et al. 2021

Water

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Artificial Neural Networks (ANNs) have wide applications in aquatic ecology and specifically in modelling water quality and biotic responses to environmental predictors. However, data scarcity is a common problem that raises the need to optimize modelling approaches to overcome data limitations. With this paper, we investigate the optimal k-fold cross validation in building an ANN using a small water-quality data set. The ANN was created to model the chlorophyll-a levels of a shallow eutrophic lake (Mikri Prespa) located in N. Greece. The typical water quality parameters serving as the ANN’s inputs are pH, dissolved oxygen, water temperature, phosphorus, nitrogen, electric conductivity, and Secchi disk depth. The available data set was small, containing only 89 data samples. For that reason, k-fold cross validation was used for training the ANN. To find the optimal k value for the k-fold cross validation, several values of k were tested (ranging from 3 to 30). Additionally, the leave-one-out (LOO) cross validation, which is an extreme case of the k-fold cross validation, was also applied. The ANN’s performance indices showed a clear trend to be improved as the k number was increased, while the best results were calculated for the LOO cross validation as expected. The computational times were calculated for each k value, where it was found the computational time is relatively low when applying the more expensive LOO cross validation; therefore, the LOO is recommended. Finally, a sensitivity analysis was examined using the ANN to investigate the interactions of the input parameters with the Chlorophyll-a, and hence examining the potential use of the ANN as a water management tool for nutrient control.

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Section: Preliminaries On Ann and Model Constructionmentioning

confidence: 99%

Modelling Freshwater Eutrophication with Limited Limnological Data Using Artificial Neural Networks

Hadjisolomou

Stefanidis

Herodotou

et al. 2021

Water

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“…Recently, one of the most common artificial neural networks (ANNs), the multilayer perceptron (MLP) developed by Pal and Mitra (1992) , has been broadly used for modeling and predicting complex traits, such as yield, in different breeding programs ( Geetha, 2020 ). MLP can be considered as a non-linear computational method employed for various tasks such as classification and regression of complex systems ( Chen and Wang, 2020 ; Hesami et al, 2020b ). This algorithm is able to detect the connection and relationship between the input and output (target) variables and quantify the inherent knowledge existing in the datasets ( Ghorbani et al, 2016 ; Hesami et al, 2020b ).…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean

Yoosefzadeh-Najafabadi

et al. 2021

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Recent substantial advances in high-throughput field phenotyping have provided plant breeders with affordable and efficient tools for evaluating a large number of genotypes for important agronomic traits at early growth stages. Nevertheless, the implementation of large datasets generated by high-throughput phenotyping tools such as hyperspectral reflectance in cultivar development programs is still challenging due to the essential need for intensive knowledge in computational and statistical analyses. In this study, the robustness of three common machine learning (ML) algorithms, multilayer perceptron (MLP), support vector machine (SVM), and random forest (RF), were evaluated for predicting soybean (Glycine max) seed yield using hyperspectral reflectance. For this aim, the hyperspectral reflectance data for the whole spectra ranged from 395 to 1005 nm, which were collected at the R4 and R5 growth stages on 250 soybean genotypes grown in four environments. The recursive feature elimination (RFE) approach was performed to reduce the dimensionality of the hyperspectral reflectance data and select variables with the largest importance values. The results indicated that R5 is more informative stage for measuring hyperspectral reflectance to predict seed yields. The 395 nm reflectance band was also identified as the high ranked band in predicting the soybean seed yield. By considering either full or selected variables as the input variables, the ML algorithms were evaluated individually and combined-version using the ensemble–stacking (E–S) method to predict the soybean yield. The RF algorithm had the highest performance with a value of 84% yield classification accuracy among all the individual tested algorithms. Therefore, by selecting RF as the metaClassifier for E–S method, the prediction accuracy increased to 0.93, using all variables, and 0.87, using selected variables showing the success of using E–S as one of the ensemble techniques. This study demonstrated that soybean breeders could implement E–S algorithm using either the full or selected spectra reflectance to select the high-yielding soybean genotypes, among a large number of genotypes, at early growth stages.

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“…Dalam algoritma MLP, backpropagation digunakan untuk menurunkan nilai error antara lapisan output dan kelas target [4], dan algoritma ini juga memanfaatkan fungsi aktivasi pada hidden layer untuk mengurangi nilai error dari output yang dihasilkan masing-masing neuron [5]. Fungsi aktivasi ReLu [6] dan Tanh [7] merupakan dua fungsi aktivasi yang cukup populer dan banyak digunakan pada arsitektur MLP, karena dapat menghasilkan waktu pelatihan data yang lebih cepat. Penelitian ini bertujuan untuk menganalisa hasil klasifikasi algoritma MLP dengan menggunakan kedua fungsi aktivasi tersebut dan variasi jumlah hidden layer yang berbeda.…”

Section: Pendahuluanunclassified

Komparasi Fungsi Aktivasi Relu Dan Tanh Pada Multilayer Perceptron

Firmansyah¹,

Hayadi²

2022

JIKO

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Neural network merupakan metode yang populer digunakan dalam penelitian di bidang machine, dan fungsi aktivasi, khususnya ReLu dan Tanh, memiliki fungsi yang sangat penting pada neural network, untuk meminimalisir nilai error antara lapisan output dan kelas target. Dengan variasi jumlah hidden layer, serta jumlah neuron pada masing-masing hidden layer yang berbeda, penelitian ini menganalisa 8 model untuk mengklasifikasikan dataset Titanic's Survivor. Diperoleh hasil bahwa fungsi ReLu memiliki kinerja yang lebih baik daripada Tanh, dilihat dari nilai rata-rata akurasi dan presisinya yang lebih tinggi dibandingkan fungsi aktivasi Tanh. Penambahan jumlah hidden layer tidak berpengaruh pada meningkatnya kinerja hasil klasifikasi, terlihat dari menurunnya rata-rata akurasi dan presisi dari model yang menggunakan 3 hidden layer dan model yang menggunakan 4 hidden layer. Nilai akurasi tertinggi diperoleh pada model yang menggunakan fungsi aktivasi ReLu dengan 4 hidden layer dan 50 neuron pada masing-masing hidden layer, sedangkan nilai presisi tertinggi diperoleh pada model yang menggunakan fungsi aktivasi ReLu dengan 4 hidden layer dan 100 neuron pada masing-masing hidden layer.

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Comparing Activation Functions in Modeling Shoreline Variation Using Multilayer Perceptron Neural Network

Cited by 26 publications

References 34 publications

Modelling Freshwater Eutrophication with Limited Limnological Data Using Artificial Neural Networks

Modelling Freshwater Eutrophication with Limited Limnological Data Using Artificial Neural Networks

Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean

Komparasi Fungsi Aktivasi Relu Dan Tanh Pada Multilayer Perceptron

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