Under mild regularity conditions, a dynamical system can be approximated to any accuracy by a recurrent neural network (NN) [1]. This universal approximation property qualifies recurrent NNs as system identifiers in the parallel formulation. If a dynamical system under identification is affected by an uncertain environmental parameter, online adjustment of the weights of the system identifier is necessary to adapt to the environmental parameter in the parallel formulation. However, adjusting all the weights of a recurrent NN involves much online computation, poor local minima to fall into, long transient states, and even divergence of the output of the recurrent NN. This motivated the development of adaptive multilayer perceptrons with long- and short-term memories (i.e. MLPWINs with long-and short-term memories), which are intended to eliminate all these difficulties. Mathematical justification of adaptive MLPWINs for parallel system identification was reported in [2]. In this paper, numerical feasibility of the same is studied. Simple dynamical systems selected from three classes of dynamical systems, namely systems with nonlinear actuation, bilinear systems, and systems with bifurcation and chaos, are used in our study. In all three examples, it is shown that adaptive MLPWINs properly trained as adaptive parallel identifiers adapt effectively to changing environmental parameters even with values not included in the a priori offline training data.
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