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- W1982522398 abstract "This paper proposes an integrated general regression neural network (GRNN) adaptation scheme for dynamic plant modelling. The scheme can be used in a noisy and dynamic environment for on-line process control. It possesses several distinguished features compared with the original GRNN proposed by Specht, such as a flexible pattern nodes add-in and delete-off mechanism, a dynamic initial sigma assignment using a non-statistical method, automatic target adjustment and sigma tuning. These adaptation strategies are formulated on the basis of the inherent advantageous features found in GRNN, such as highly localized pattern nodes, good interpolation capability and instantaneous learning. Good modelling performance was obtained when the GRNN is tested on a linear plant in a noisy environment. It performs better than the well-known extended recursive least-squares identification algorithm. In this paper, analysis of the effects of some of the adaptation parameters involving a non-linear plant is also investigated. The results show that the proposed methodology is computationally efficient and exhibits several attractive features such as fast learning, flexible network sizing and good robustness, which are suitable for the construction of estimators or predictors for many model-based adaptive control strategies." @default.
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- W1982522398 date "1999-06-01" @default.
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- W1982522398 title "Adaptive general regression neural network for modelling of dynamic plants" @default.
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