scholarly journals Nonlinear models for recognition of concentration of mixture

2021 ◽  
Vol 47 ◽  
Author(s):  
Feliksas Ivanauskas ◽  
Robertas Paulauskas ◽  
Pranas Vaitkus
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector Regression ◽  
Extreme Learning Machine ◽  
Nonlinear Models ◽  
Support Vector ◽  
Artificial Neural ◽  
Learning Machine

In this paper extreme learning machine and support vector regression are used for biosensors response to mixtures of compounds classification. The results are compared with the results obtained using artificial neural networks and others.

scholarly journals Model Ensembles of Artificial Neural Networks and Support Vector Regression for Improved Accuracy in the Prediction of Vegetation Conditions and Droughts in Four Northern Kenya Counties

2019 ◽  
Vol 8 (12) ◽  
pp. 562 ◽  
Author(s):  
Chrisgone Adede ◽  
Robert Oboko ◽  
Peter W. Wagacha ◽  
Clement Atzberger
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector Regression ◽  
Prediction Models ◽  
Weighted Averaging ◽  
Support Vector ◽  
Heterogeneous Model ◽  
Drought Prediction ◽  
Artificial Neural ◽  
Model Ensembles

For improved drought planning and response, there is an increasing need for highly predictive and stable drought prediction models. This paper presents the performance of both homogeneous and heterogeneous model ensembles in the satellite-based prediction of drought severity using artificial neural networks (ANN) and support vector regression (SVR). For each of the homogeneous and heterogeneous model ensembles, the study investigates the performance of three model ensembling approaches: (1) non-weighted linear averaging, (2) ranked weighted averaging, and (3) model stacking using artificial neural networks. Using the approach of “over-produce then select”, the study used 17 years of satellite data on 16 selected variables for predictive drought monitoring to build 244 individual ANN and SVR models from which 111 models were automatically selected for the building of the model ensembles. Model stacking is shown to realize models that are superior in performance in the prediction of future drought conditions as compared to the linear averaging and weighted averaging approaches. The best performance from the heterogeneous stacked model ensembles recorded an R2 of 0.94 in the prediction of future (1 month ahead) vegetation conditions on unseen test data (2016–2017) as compared to an R2 of 0.83 and R2 of 0.78 for ANN and SVR, respectively, in the traditional approach of selection of the best (champion) model. We conclude that despite the computational resource intensiveness of the model ensembling approach, the returns in terms of model performance for drought prediction are worth the investment, especially in the context of the continued exponential increase in computational power and the potential benefits of improved forecasting for vulnerable populations.

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