THE COMPUTATION OF INVERSE TIME VARIANT FUNCTIONS VIA PROPER PSEUDOINVERSE BOUNDING: A RADIAL BASIS FUNCTION NETWORK APPROACH

摘要

In this article a Radial Basis Function Network (RBFN) approach for fast and efficient computation of inverse continuous time variant functions is presented. The approach is based on using a novel RBFN approach for computing inverse continuous time variant functions via a damped least squares formulation and also on a non-conventional implementation of an original approach for singularities prevention and conditioning improvement. The singularities avoidance approach in turn consists on establishing some characterizing matrices, in order to obtain a performance index and a null space vector, and then properly including it hi the overall RBFN approach.