A Novel Optimization Strategy for the Nonlinear Systems Identification
Computational Intelligence and Security
Nonlinear system identification based on delta-learning rules
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
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This paper compares the approximation accuracy of two basis functions that share a common radial basis function (RBF) neural network architecture used for approximating a known function on the unit sphere. The basis function types considered are that of a new spherical basis function, the von Mises function, and the now well-known Gaussian basis function. Gradient descent learning rules were applied to optimize (learn) the solution for both approximating basis functions. A benchmark approximation problem was used to compare the performance of the two types of basis functions, in this case the mathematical expression for the scattering of an acoustic wave striking a rigid sphere