A novel machine learning algorithm and its use in the modelling and simulation of dynamical systems

A machine learning method, suitable for applications in domains involving complex nonlinear systems, is presented. The learning algorithm is used to construct a kind of associative memory which features a sophisticated local interpolation scheme and fast searching algorithms. Experiments with the implemented algorithm in the acquisition of the dynamics model for the well-known pole-balancing system verify the algorithm's theoretically derived time and space requirements and demonstrate its efficiency.<
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