Regression with Gaussian-Type Responses

2002 ◽  
pp. 59-110
Author(s):  
Chong Gu
Keyword(s):  
Gaussian Type

A closed form of a kurtosis parameter of a hypergeometric-Gaussian type-II beam

2016 ◽  
Vol 25 (4) ◽  
pp. 044206 ◽  
Author(s):  
F Khannous ◽  
A A A Ebrahim ◽  
A Belafhal
Keyword(s):  
Closed Form ◽  
Type Ii ◽  
Gaussian Type ◽  
Kurtosis Parameter

scholarly journals Curve crossing problem with Gaussian type coupling: analytically solvable model

2012 ◽  
Vol 110 (18) ◽  
pp. 2197-2203 ◽  
Author(s):  
Diwaker ◽  
Aniruddha Chakraborty
Keyword(s):  
Solvable Model ◽  
Gaussian Type ◽  
Type Coupling ◽  
Curve Crossing

scholarly journals Non-Gaussian Hybrid Transfer Functions: Memorizing Mine Survivability Calculations

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Mary Opokua Ansong ◽  
Jun Steed Huang ◽  
Mary Ann Yeboah ◽  
Han Dun ◽  
Hongxing Yao
Keyword(s):  
Hybrid Model ◽  
Transfer Functions ◽  
Mean Squared Error ◽  
Network Efficiency ◽  
Gaussian Type ◽  
Squared Error ◽  
The Neural Network ◽  
Radial Basis ◽  
Non Gaussian ◽  
Complexity Increase

Hybrid algorithms and models have received significant interest in recent years and are increasingly used to solve real-world problems. Different from existing methods in radial basis transfer function construction, this study proposes a novel nonlinear-weight hybrid algorithm involving the non-Gaussian type radial basis transfer functions. The speed and simplicity of the non-Gaussian type with the accuracy and simplicity of radial basis function are used to produce fast and accurate on-the-fly model for survivability of emergency mine rescue operations, that is, the survivability under all conditions is precalculated and used to train the neural network. The proposed hybrid uses genetic algorithm as a learning method which performs parameter optimization within an integrated analytic framework, to improve network efficiency. Finally, the network parameters including mean iteration, standard variation, standard deviation, convergent time, and optimized error are evaluated using the mean squared error. The results demonstrate that the hybrid model is able to reduce the computation complexity, increase the robustness and optimize its parameters. This novel hybrid model shows outstanding performance and is competitive over other existing models.

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