Associative Memory Network Based Modelling of Unknown Nonlinear Systems subject to Immeasurable Disturbances

Wang, H., Brown, M. and Harris, C.J. (1994) Associative Memory Network Based Modelling of Unknown Nonlinear Systems subject to Immeasurable Disturbances. IEE Part D, 141 (4), 216-222.

Record type: Article

Abstract

This paper presents a neural network based scheme for modelling unknown nonlinear systems subject to immeasurable disturbances which satisfy stable, finite-order, recurrence relationships whose parameters are known. The systems considered can be expressed as nonlinear ARMAX models and the disturbance is non-stochastic. Similar to robust servomechanism design, the nonlinear modes of the disturbance are assumed to be known and based upon the knowledge of these modes, a new performance function for modelling the unknown nonlinear function is selected and a gradient descent algorithm which adjusts the weights in the neural network is derived. Convergence of this learning algorithm is proved when the disturbance satisfies a linear recurrence relationship, and the proposed approach is used to model nonlinear time series data which has been corrupted by immeasurable additive sinusoidal noise.

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