This paper proposes a specific type of Local Linear Model, the Randomly Local

Linear Model (RLLM), 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 RLLM and an

upper bound on the number of models are proved mathematically, and an efficient

algorithm is proposed.

**Tutaj masz dostęp do tysięcy prac naukowych. Skorzystaj (w języku publikacji) z wyszukiwarki powyżej.**