Multilayer feedforward networks are universal approximators
Neural Networks
Paper: Volterra series and geometric control theory
Automatica (Journal of IFAC)
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
Self-organizing network for optimum supervised learning
IEEE Transactions on Neural Networks
The Stone-Weierstrass theorem and its application to neural networks
IEEE Transactions on Neural Networks
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This work introduces a new nonlinear computational model called polynomial network for identification and adaptive control of nonlinear dynamical systems. The network approximates the Volterra systems or recursive polynomial systems. The approximation properties of this network are compared with the sigmoid networks. The results show that the polynomial network constructs a simpler and smaller model and requires less training data. Also, the model realized by the polynomial network is mathematically tractable. The feasibility of using this model for direct model reference adaptive control of a class of nonlinear systems is demonstrated.