Authors: Nafiz Aydın HIZAL
Abstract: In this paper, an algorithm which can generate a nonlineer multi-input function through a learning process is studied. This algorithm, used by the Sulzer A.G. company for building heating automation, has apparent potentials for use in control and diagnostics. For this reason, some of its fundamental properties like accuracy, stability and convergence rate which were considered to be important regarding practical design were investigated, and the results are reported here.
Keywords: Learning systems, Trainable Function Generator, Nonlinear Function Generator.
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