Abstract
There exists a need to develop better mathematical models for physical processes, but it is also essential to understand that it is impossible to describe real-world industrial processes by exact mathematical models. Pattern recognition based methodology implemented in the architecture of artificial neural network can be used to model knowledge intensive feedback control systems. The procedure for development and practical design of neural-net based control systems is described and demonstrated by the example of a nonlinear hydraulic system. The results obtained in computer simulations and experiments are presented to illustrate the new approach.
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