A data-driven approach towards the full anthropometric measurements prediction via Generalized Regression Neural Networks

2021 ◽  
pp. 107551
Author(s):  
Lining Wang ◽  
Tien Ju Lee ◽  
Jan Bavendiek ◽  
Lutz Eckstein
Keyword(s):  
Neural Networks ◽  
Anthropometric Measurements ◽  
Data Driven ◽  
Generalized Regression Neural Networks ◽  
Data Driven Approach ◽  
Generalized Regression

scholarly journals Assessment of rainfall aggregation and disaggregation using data-driven models and wavelet decomposition

2016 ◽  
Vol 48 (1) ◽  
pp. 99-116 ◽  
Author(s):  
Sungwon Kim ◽  
Ozgur Kisi ◽  
Youngmin Seo ◽  
Vijay P. Singh ◽  
Chang-Joon Lee
Keyword(s):  
Neural Networks ◽  
Support Vector Machines ◽  
Wavelet Decomposition ◽  
Data Driven ◽  
Support Vector ◽  
Generalized Regression Neural Networks ◽  
Vector Machines ◽  
Aggregation And Disaggregation ◽  
Generalized Regression ◽  
Mother Wavelets

The objective of this study is to develop hybrid models by combining data-driven models, including support vector machines (SVM) and generalized regression neural networks (GRNN), and wavelet decomposition for aggregation and disaggregation of rainfall. The wavelet-based support vector machines (WSVM) and wavelet-based generalized regression neural networks (WGRNN) models are obtained using mother wavelets, including db8, db10, sym8, sym10, coif6, and coif12. The developed models are evaluated in the Bocheong-stream catchment, an International Hydrological Program representative catchment, Republic of Korea. WSVM and WGRNN models with mother wavelet db10 yield the best performance as compared with other mother wavelets for estimating areal and disaggregated rainfalls, respectively. Among 12 rainfall stations, SVM, GRNN, WSVM (db10 and sym10), and WGRNN (db10 and sym10) models provide the best accuracies for estimating the disaggregated rainfalls at Samga (No. 7), and the worst accuracies for estimating the disaggregated rainfalls at Yiweon (No. 11) stations, respectively. Results obtained from this study indicate that the combination of data-driven models and wavelet decomposition can be a useful tool for estimating areal and disaggregated rainfalls satisfactorily, and can yield better efficiency than data-driven models.

scholarly journals Bandwidth Selection for Kernel Generalized Regression Neural Networks in Identification of Hammerstein Systems

2021 ◽  
Vol 11 (3) ◽  
pp. 181-194
Author(s):  
Jiaqing Lv ◽  
Mirosław Pawlak
Keyword(s):  
Neural Networks ◽  
Bandwidth Selection ◽  
Identification Problem ◽  
Data Driven ◽  
Identification Algorithm ◽  
Statistical Accuracy ◽  
Practical Procedure ◽  
System A ◽  
Generalized Regression Neural Networks ◽  
Generalized Regression

Abstract This paper addresses the issue of data-driven smoothing parameter (bandwidth) selection in the context of nonparametric system identification of dynamic systems. In particular, we examine the identification problem of the block-oriented Hammerstein cascade system. A class of kernel-type Generalized Regression Neural Networks (GRNN) is employed as the identification algorithm. The statistical accuracy of the kernel GRNN estimate is critically influenced by the choice of the bandwidth. Given the need of data-driven bandwidth specification we propose several automatic selection methods that are compared by means of simulation studies. Our experiments reveal that the method referred to as the partitioned cross-validation algorithm can be recommended as the practical procedure for the bandwidth choice for the kernel GRNN estimate in terms of its statistical accuracy and implementation aspects.

scholarly journals Cardiac Activation Maps Reconstruction: A Comparative Study Between Data-Driven and Physics-Based Methods

2021 ◽  
Vol 12 ◽  
Author(s):  
Amel Karoui ◽  
Mostafa Bendahmane ◽  
Nejib Zemzemi
Keyword(s):  
Neural Networks ◽  
Comparative Study ◽  
Body Surface ◽  
Data Driven ◽  
Diagnostic Tools ◽  
L1 Norm ◽  
Surface Potentials ◽  
Body Surface Potential ◽  
Data Driven Approach ◽  
Classic Technique

One of the essential diagnostic tools of cardiac arrhythmia is activation mapping. Noninvasive current mapping procedures include electrocardiographic imaging. It allows reconstructing heart surface potentials from measured body surface potentials. Then, activation maps are generated using the heart surface potentials. Recently, a study suggests to deploy artificial neural networks to estimate activation maps directly from body surface potential measurements. Here we carry out a comparative study between the data-driven approach DirectMap and noninvasive classic technique based on reconstructed heart surface potentials using both Finite element method combined with L1-norm regularization (FEM-L1) and the spatial adaptation of Time-delay neural networks (SATDNN-AT). In this work, we assess the performance of the three approaches using a synthetic single paced-rhythm dataset generated on the atria surface. The results show that data-driven approach DirectMap quantitatively outperforms the two other methods. In fact, we observe an absolute activation time error and a correlation coefficient, respectively, equal to 7.20 ms, 93.2% using DirectMap, 14.60 ms, 76.2% using FEM-L1 and 13.58 ms, 79.6% using SATDNN-AT. In addition, results show that data-driven approaches (DirectMap and SATDNN-AT) are strongly robust against additive gaussian noise compared to FEM-L1.

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