scholarly journals Neural Network Training for Prediction of Climatological Time Series, Regularized by Minimization of the Generalized Cross-Validation Function

2000 ◽  
Vol 128 (5) ◽  
pp. 1456-1473 ◽  
Author(s):  
Yuval
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Cross Validation ◽  
Neural Network Training ◽  
Generalized Cross Validation ◽  
Network Training

Daily Load Forecasting Based on Combination Forecasting Techniques

2011 ◽  
Vol 201-203 ◽  
pp. 2685-2689
Author(s):  
Chong Gao ◽  
Hai Jie Ma ◽  
Pei Na Gao
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Load Forecasting ◽  
Ar Model ◽  
Neural Network Training ◽  
Network Training ◽  
Artificial Neural ◽  
Daily Load ◽  
Forecasting Techniques

To improve the accuracy of load forecasting is the focus of the load forecasting. As the daily load by various environmental factors and periodical, this makes the load time series of changes occurring during non-stationary random process. The key of improving the accuracy of artificial neural network training is to select effective training sample. This paper based on the time series forecasting techniques’ random time series autocorrelation function to select the neural network training samples. The method of modeling is more objective. By example, the comparison with autoregressive (AR) Model predictions and BP Artificial Neural Network (ANN) predicted results through error analysis and confirmed the proposed scheme good performance.

scholarly journals A New Methodology for Neural Network Training Ensures Error Reduction in Time Series Forecasting

2017 ◽  
Vol 13 (7) ◽  
pp. 211-217 ◽  
Author(s):  
Paola A. Sánchez-Sánchez ◽  
José Rafael García-González
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Error Reduction ◽  
Time Series Forecasting ◽  
Neural Network Training ◽  
Network Training

scholarly journals A Geometric Perspective on Information Plane Analysis

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 711
Author(s):  
Mina Basirat ◽  
Bernhard C. Geiger ◽  
Peter M. Roth
Keyword(s):  
Neural Network ◽  
Mutual Information ◽  
Geometric Interpretation ◽  
Neural Network Training ◽  
Neural Network Learning ◽  
Network Learning ◽  
Plane Analysis ◽  
Network Training ◽  
Hidden Layer ◽  
The Impact

Information plane analysis, describing the mutual information between the input and a hidden layer and between a hidden layer and the target over time, has recently been proposed to analyze the training of neural networks. Since the activations of a hidden layer are typically continuous-valued, this mutual information cannot be computed analytically and must thus be estimated, resulting in apparently inconsistent or even contradicting results in the literature. The goal of this paper is to demonstrate how information plane analysis can still be a valuable tool for analyzing neural network training. To this end, we complement the prevailing binning estimator for mutual information with a geometric interpretation. With this geometric interpretation in mind, we evaluate the impact of regularization and interpret phenomena such as underfitting and overfitting. In addition, we investigate neural network learning in the presence of noisy data and noisy labels.

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