  This paper proposes a specific type of Local Linear Model, the Shuffled
Linear Model (SLM), that can be used as a universal approximator. Local
operating points are chosen randomly and linear models are used to approximate
a function or system around these points. The model can also be interpreted as
an extension to Extreme Learning Machines with Radial Basis Function nodes, or
as a specific way of using Takagi-Sugeno fuzzy models. Using the available
theory of Extreme Learning Machines, universal approximation of the SLM and an
upper bound on the number of models are proved mathematically, and an efficient
algorithm is proposed.
