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Local model networks for nonlinear system identification
Author(s): M.D. Brown, G. Lightbody, G.W. Irwin
Source: IEE Colloquium on Industrial Applications of Intelligent Control, 1997, p. 4 - 4
Type: ConferencePaper
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Local model networks represent a nonlinear dynamical system by a set of locally valid submodels across the operating range. Training such feedforward structures involves the combined estimation of the submodel parameters and those of the interpolation functions. The paper describes a new hybrid learning approach for local model networks that uses a combination of singular value decomposition and second order gradient optimization. A new nonlinear internal model control scheme is proposed which has the important property that the controller can be derived analytically. Simulation studies of a pH neutralization process confirm the excellent modelling and control performance using the local model approach. (3 pages)

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