Restoring high frequency spectral envelopes using neural networks for speech bandwidth extension

2015 ◽  
Author(s):  
Yu Gu ◽  
Zhen-Hua Ling
Keyword(s):  
Neural Networks ◽  
High Frequency ◽  
Bandwidth Extension

scholarly journals Uncertainty Due to Infectious Diseases and Stock–Bond Correlation

Econometrics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 17
Author(s):  
Konstantinos Gkillas ◽  
Christoforos Konstantatos ◽  
Costas Siriopoulos
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Infectious Diseases ◽  
Granger Causality ◽  
Predictive Value ◽  
High Frequency ◽  
Causal Relation ◽  
Granger Causality Test ◽  
Causality Test ◽  
Non Linear

We study the non-linear causal relation between uncertainty-due-to-infectious-diseases and stock–bond correlation. To this end, we use high-frequency 1-min data to compute daily realized measures of correlation and jumps, and then, we employ a nonlinear Granger causality test with the use of artificial neural networks so as to investigate the predictability of this type of uncertainty on realized stock–bond correlation and jumps. Our findings reveal that uncertainty-due-to-infectious-diseases has significant predictive value on the changes of the stock–bond relation.

Ultra High Frequency Sigmoid and Trigonometric Higher Order Neural Networks for Data Pattern Recognition

2016 ◽  
pp. 80-112 ◽  
Author(s):  
Ming Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Nonlinear Model ◽  
High Frequency ◽  
Higher Order ◽  
Sine Function ◽  
Ultra High Frequency ◽  
Higher Order Neural Networks ◽  
Cosine Functions

This chapter develops a new nonlinear model, Ultra high frequency siGmoid and Trigonometric Higher Order Neural Networks (UGT-HONN), for data pattern recognition. UGT-HONN includes Ultra high frequency siGmoid and Sine function Higher Order Neural Networks (UGS-HONN) and Ultra high frequency siGmoid and Cosine functions Higher Order Neural Networks (UGC-HONN). UGS-HONN and UGC-HONN models are used to recognition data patterns. Results show that UGS-HONN and UGC-HONN models are better than other Polynomial Higher Order Neural Network (PHONN) and Trigonometric Higher Order Neural Network (THONN) models, since UGS-HONN and UGC-HONN models to recognize data pattern with error approaching 0.0000%.

Ultra High Frequency Sigmoid and Trigonometric Higher Order Neural Networks for Data Pattern Recognition

2016 ◽  
pp. 682-715
Author(s):  
Ming Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Nonlinear Model ◽  
High Frequency ◽  
Higher Order ◽  
Sine Function ◽  
Ultra High Frequency ◽  
Higher Order Neural Networks ◽  
Cosine Functions

This chapter develops a new nonlinear model, Ultra high frequency siGmoid and Trigonometric Higher Order Neural Networks (UGT-HONN), for data pattern recognition. UGT-HONN includes Ultra high frequency siGmoid and Sine function Higher Order Neural Networks (UGS-HONN) and Ultra high frequency siGmoid and Cosine functions Higher Order Neural Networks (UGC-HONN). UGS-HONN and UGC-HONN models are used to recognition data patterns. Results show that UGS-HONN and UGC-HONN models are better than other Polynomial Higher Order Neural Network (PHONN) and Trigonometric Higher Order Neural Network (THONN) models, since UGS-HONN and UGC-HONN models to recognize data pattern with error approaching 0.0000%.

Data Pattern Recognition Based on Ultra-High Frequency Sigmoid and Trigonometric Higher Order Neural Networks

2021 ◽  
pp. 455-497
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Nonlinear Model ◽  
High Frequency ◽  
Higher Order ◽  
Sine Function ◽  
Ultra High Frequency ◽  
Higher Order Neural Networks ◽  
Cosine Functions

This chapter develops a new nonlinear model, ultra high frequency sigmoid and trigonometric higher order neural networks (UGT-HONN), for data pattern recognition. UGT-HONN includes ultra high frequency sigmoid and sine function higher order neural networks (UGS-HONN) and ultra high frequency sigmoid and cosine functions higher order neural networks (UGC-HONN). UGS-HONN and UGC-HONN models are used to recognition data patterns. Results show that UGS-HONN and UGC-HONN models are better than other polynomial higher order neural network (PHONN) and trigonometric higher order neural network (THONN) models, since UGS-HONN and UGC-HONN models can recognize data pattern with error approaching 10-6.

Time Series Data Analysis by Ultra-High Frequency Trigonometric Higher Order Neural Networks

2021 ◽  
pp. 218-261
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Exchange Rates ◽  
High Frequency ◽  
Time Series Data ◽  
Higher Order ◽  
Series Data ◽  
Ultra High Frequency ◽  
Higher Order Neural Networks ◽  
Time Series Data Analysis

This chapter develops a new nonlinear model, ultra high frequency trigonometric higher order neural networks (UTHONN) for time series data analysis. UTHONN includes three models: UCSHONN (ultra high frequency sine and cosine higher order neural networks) models, UCCHONN (ultra high frequency cosine and cosine higher order neural networks) models, and USSHONN (ultra high frequency sine and sine higher order neural networks) models. Results show that UTHONN models are 3 to 12% better than equilibrium real exchange rates (ERER) model, and 4–9% better than other polynomial higher order neural network (PHONN) and trigonometric higher order neural network (THONN) models. This study also uses UTHONN models to simulate foreign exchange rates and consumer price index with error approaching 10-6.

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