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Neural Control Experiments via Dynamic Neural Algorithms by J. Fernández De Cañete; A. García-Cerezo; I. García-Moral is a Engineering article available to read on EtoBox.

What is Neural Control Experiments via Dynamic Neural Algorithms about?

Neural static and dynamic training algorithms have been extensively applied to the control and identi"cation of non-linear dynamic plants. In the present paper an extension of the static Marquardt learning algorithm, termed Dynamic Marquardt algorithm (DMA) is derived for the on-line training of neural networks with feedforward and feedback components. The performance of the method has been demonstrated by the neural control of a highly non-linear experimental #uid level. A stability analysis of the overall control scheme has been carried out using the conicity stability criterion. It has been found that the Dynamic Marquardt algorithm is much more e$cient than Dynamic backpropagation, when the relative size of the net is bounded.

Who reads Neural Control Experiments via Dynamic Neural Algorithms?

Author

J. Fernández De Cañete; A. García-Cerezo; I. García-Moral

Publisher

John Wiley and Sons; Wiley (John Wiley & Sons); John Wiley & Sons Inc.; Wiley (ISSN 0890-6327)

Published

1999

Language

EN

Field

Engineering (Physical Sciences)