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- W2039633247 abstract "An earlier paper introduced the MANNIDENT (multivariable artificial neural network identification) approach to process modelling in which approximate linear models determine the topology and initial weights of a neural network and which provides a structure for interpretation of the final model. This paper introduces the MANNCON (multivariable artificial neural network control) system, in which these models are incorporated into model-based controllers. Designs using internal model control (IMC) and direct synthesis control (DSC) as well as a supervisory ANN-based controller are presented. These methods are applied to the task of controlling a non-isothermal CSTR in which a first-order exothermic reaction is occurring. The nonlinear ANN-based controllers perform much better than their linear counterparts in controlling the process over a wide range of conditions. Furthermore, the superivosyr ANN controller showed better robustness properties with respect to disturbance rejection and to plant-model mismatch than the other controllers studied." @default.
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- W2039633247 date "1993-01-01" @default.
- W2039633247 modified "2023-09-23" @default.
- W2039633247 title "Experiences with model-based controllers based on neural network process models" @default.
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- W2039633247 doi "https://doi.org/10.1016/0959-1524(93)80023-5" @default.
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