A spherical basis function neural network for approximating acoustic scatter

1996 ◽  
Vol 99 (5) ◽  
pp. 3242-3245 ◽  
Author(s):  
Rick L. Jenison
Keyword(s):  
Neural Network ◽  
Basis Function ◽  
Spherical Basis ◽  
Spherical Basis Function

A Spherical Basis Function Neural Network for Modeling Auditory Space

1996 ◽  
Vol 8 (1) ◽  
pp. 115-128 ◽  
Author(s):  
Rick L. Jenison ◽  
Kate Fissell
Keyword(s):  
Neural Network ◽  
Basis Function ◽  
Gradient Descent ◽  
Acoustic Parameters ◽  
Auditory Space ◽  
Learning Rules ◽  
Spherical Basis ◽  
Von Mises ◽  
Processing Engine ◽  
Spherical Basis Function

This paper describes a neural network for approximation problems on the sphere. The von Mises basis function is introduced, whose activation depends on polar rather than Cartesian input coordinates. The architecture of the von Mises Basis Function (VMBF) neural network is presented along with the corresponding gradient-descent learning rules. The VMBF neural network is used to solve a particular spherical problem of approximating acoustic parameters used to model perceptual auditory space. This model ultimately serves as a signal processing engine to synthesize a virtual auditory environment under headphone listening conditions. Advantages of the VMBF over standard planar Radial Basis Functions (RBFs) are discussed.

Cubature formula for spherical basis function networks

2012 ◽  
Vol 33 (6) ◽  
pp. 807-814
Author(s):  
Shaobo Lin ◽  
Feilong Cao ◽  
Zongben Xu ◽  
Xiaofei Guo
Keyword(s):  
Basis Function ◽  
Cubature Formula ◽  
Spherical Basis ◽  
Spherical Basis Function

ε-Insensitive Criterion and Structure Risk Based Radius-basis-function Neural-network Modeling

2012 ◽  
Vol 34 (6) ◽  
pp. 1414-1419
Author(s):  
Qing-bing Sang ◽  
Zhao-hong Deng ◽  
Shi-tong Wang ◽  
Xiao-jun Wu
Keyword(s):  
Neural Network ◽  
Basis Function ◽  
Network Modeling ◽  
Neural Network Modeling
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