R.V. Mayorga and J. Carrera (Canada)
In this article a Radial Basis Function Net work (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 noncon ventional implementation of an original approach for sin gularities prevention and conditioning improvement. The singularities avoidance approach in turn consists on estab lishing some characterizing matrices, in order to obtain a performance index and a null space vector, and then prop erly including it in the overall RBFN approach.
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