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Universal Approximation of Multiple Nonlinear Operators by Neural Networks

back{at}windale.com, Windale Technologies, Brisbane, QLD 4075, Australia

Tianping Chen

tpchenk{at}online.sh.cn, Department of Mathematics, Fudan University, Shanghai, 200433, China

Recently, there has been interest in the observed capabilities of some classes of neural networks with fixed weights to model multiple nonlinear dynamical systems. While this property has been observed in simulations, open questions exist as to how this property can arise. In this article, we propose a theory that provides a possible mechanism by which this multiple modeling phenomenon can occur.

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