Annual Earthquake Potential Consultation: A Real Forward Prediction Test in China

Author(s):  
Yongxian Zhang ◽  
Zhongliang Wu ◽  
Xiaotao Zhang ◽  
Gang Li
2020 ◽  
Vol 4 (1) ◽  
pp. 1
Author(s):  
Christian Ebere Enyoh ◽  
Andrew Wirnkor Verla ◽  
Chidi Edbert Duru ◽  
Emmanuel Chinedu Enyoh ◽  
Budi Setiawan

Based on the official Nigeria Centre for Disease Control (NCDC) data, the current research paper modeled the confirmed cases of the novel coronavirus disease 2019 (COVID-19) in Nigeria. Ten different curve regression models including linear, logarithmic, inverse, quadratic, cubic, compound, power, S-curve, growth, and exponential were used to fit the obtained official data. The cubic (R2 = 0.999) model gave the best fit for the entire country. However, the growth and exponential had the lowest standard error of estimate (0.958) and thus may best be used. The equations for these models were e0.78897+0.0944x and 2.2011e0.0944x respectively. In terms of confirmed cases in individual State, quadratic, cubic, compound, growth, power and exponential models generally best describe the official data for many states except for the state of Kogi which is best fitted with S-curve and inverse models.  The error between the model and the official data curve is quite small especially for compound, power, growth and exponential models. The computed models will help to realized forward prediction and backward inference of the epidemic situation in Nigeria, and the relevant analysis help Federal and State governments to make vital decisions on how to manage the lockdown in the country.


Geology ◽  
2000 ◽  
Vol 28 (4) ◽  
pp. 384
Author(s):  
Lisa B. Grant ◽  
Karl L. Mueller ◽  
Eldon M. Gath ◽  
Rosalind Munro

2020 ◽  
Author(s):  
Carlos Villafuerte ◽  
Víctor M. Cruz-Atienza ◽  
Josué Tago ◽  
Darío Solano-Rojas ◽  
Sara Franco ◽  
...  

1999 ◽  
Vol 09 (01n02) ◽  
pp. 125-132
Author(s):  
GEUN-TAEK RYU ◽  
DAE-SUNG KIM ◽  
DAE-YOUNG LEE ◽  
SUNG-HWAN HAN ◽  
HYEON-DEOK BAE

The choice of the adaptive gain is important to the performance of LMS-based adaptive filters. Depending on application areas, the realization structure of the filters is also important. This letter presents an adaptive lattice algorithm which adjusts the adaptive gain of LMS using fuzzy if-then rules determined by matching input and output variables during adaptation procedure. In each lattice filter stage, this filter adjusts the adaptive gain as the output of the fuzzy logic which has two input variables, normalized squared forward prediction error and one step previous adaptive gain. The proposed algorithm is applied to echo canceling problem of long distance communication channel. The simulation results are compared with NLMS on TDL and lattice structures.


2020 ◽  
Author(s):  
Carlos Villafuerte ◽  
Víctor M. Cruz-Atienza ◽  
Josué Tago ◽  
Darío Solano-Rojas ◽  
Sara Franco ◽  
...  

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