Neural network techniques for fast parametric modeling of vias on multilayered circuit packages

2010 ◽  
Author(s):  
Yazi Cao ◽  
Qi-Jun Zhang
Keyword(s):  
Neural Network ◽  
Parametric Modeling

scholarly journals Dynamic Modeling and Parameters Optimization of Large Vibrating Screen with Full Degree of Freedom

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaodong Yang ◽  
Jida Wu ◽  
Haishen Jiang ◽  
Wenqiang Qiu ◽  
Chusheng Liu
Keyword(s):  
Neural Network ◽  
Elastic Deformation ◽  
Parametric Modeling ◽  
Parameters Optimization ◽  
Degree Of Freedom ◽  
Design Parameters ◽  
Response Analysis ◽  
Lateral Plate ◽  
Beam Position ◽  
Vibrating Screen

Dynamic characteristic and reliability of the vibrating screen are important indicators of large vibrating screen. Considering the influence of coupling motion of each degree of freedom, the dynamic model with six degrees of freedom (6 DOFs) of the vibrating screen is established based on the Lagrange method, and modal parameters (natural frequencies and modes of vibration) of the rigid body are obtained. The finite element modal analysis and harmonic response analysis are carried out to analyze the elastic deformation of the structure. By using the parametric modeling method, beam position is defined as a variable, and an orthogonal experiment on design is performed. The BP neural network is used to model the relationship between beam position and maximal elastic deformation of the lateral plate. Further, the genetic algorithm is used to optimize the established neural network model, and the optimal design parameters are obtained.

scholarly journals Flexible, non-parametric modeling using regularized neural networks

2022 ◽  
Author(s):  
Oskar Allerbo ◽  
Rebecka Jörnsten
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Heterogeneous Data ◽  
Parametric Modeling ◽  
Additive Models ◽  
Adaptive Lasso ◽  
Complex Data ◽  
Data Set ◽  
Model Complex ◽  
Non Parametric

AbstractNon-parametric, additive models are able to capture complex data dependencies in a flexible, yet interpretable way. However, choosing the format of the additive components often requires non-trivial data exploration. Here, as an alternative, we propose PrAda-net, a one-hidden-layer neural network, trained with proximal gradient descent and adaptive lasso. PrAda-net automatically adjusts the size and architecture of the neural network to reflect the complexity and structure of the data. The compact network obtained by PrAda-net can be translated to additive model components, making it suitable for non-parametric statistical modelling with automatic model selection. We demonstrate PrAda-net on simulated data, where we compare the test error performance, variable importance and variable subset identification properties of PrAda-net to other lasso-based regularization approaches for neural networks. We also apply PrAda-net to the massive U.K. black smoke data set, to demonstrate how PrAda-net can be used to model complex and heterogeneous data with spatial and temporal components. In contrast to classical, statistical non-parametric approaches, PrAda-net requires no preliminary modeling to select the functional forms of the additive components, yet still results in an interpretable model representation.

ARTIFICIAL NEURAL NETWORK BASED ECG ARRHYTHMIA CLASSIFICATION

2009 ◽  
Vol 09 (04) ◽  
pp. 507-525 ◽  
Author(s):  
H. HASEENA ◽  
PAUL K. JOSEPH ◽  
ABRAHAM T. MATHEW
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Research Effort ◽  
Parametric Modeling ◽  
Back Propagation Algorithm ◽  
Ecg Signals ◽  
Feed Forward Network ◽  
Artificial Neural ◽  
Ecg Arrhythmia Classification ◽  
Ecg Arrhythmia

Reliable and computationally efficient means of classifying electrocardiogram (ECG) signals has been the subject of considerable research effort in recent years. This paper explores the potential applications of a talented, versatile computation model called the Artificial Neural Network (ANN) in the field of ECG signal classification. Two types of ANNs: Multi-Layered Feed Forward Network (MLFFN) and Probabilistic Neural Networks (PNN) are used to classify seven types of ECG beats. It includes six types of arrhythmia data and normal data. Here, parametric modeling strategies are used in conjunction with ANN classifiers to discriminate ECG signals. Instead of giving the ECG data as such, parameters such as fourth order Auto Regressive model coefficients and Spectral Entropy of the signals has been selected. On testing with the Massachusetts Institute of Technology-Beth Israel Hospital (MIT/BIH) arrhythmia database, it has been observed that PNN has better performance than conventionally used MLFFN in ECG arrhythmia classification. MLFFN with Back Propagation Algorithm gives a classification accuracy of 97.54% and PNN gives 98.96%. The classification by PNN also has an advantage that the computation time for classification is lower than that of MLFFN.

Parametric Modeling and Optimization of Phycoremediation of Cr(VI) Using Artificial Neural Network and Simulated Annealing

Algae ◽  
2020 ◽  
pp. 103-133
Author(s):  
Sushovan Sen ◽  
Abhilasha Rai ◽  
Jitamanyu Chakrabarty ◽  
Sandip Kumar Lahiri ◽  
Susmita Dutta
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Simulated Annealing ◽  
Parametric Modeling ◽  
Modeling And Optimization ◽  
Artificial Neural
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