Universal approximation using radial-basis-function networks
Neural Computation
A function estimation approach to sequential learning with neural networks
Neural Computation
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Signal Processing: Model Based Approach
Signal Processing: Model Based Approach
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A novel method for identifying a chaotic system with time-varying bifurcation parameters via an observation signal which has been contaminated by additive white Gaussian noise (AWGN) is developed. This method is based on an adaptive algorithm which takes advantage of the good approximation capability of the Radial Basis Function (RBF) neural network and the ability of the Extended Kalman Filter (EKF) for tracking a time-varying dynamical system. It is demonstrated that, provided the bifurcation parameter varies slowly in a time window, a chaotic dynamical system can be tracked and identified continuously, and the time-varying bifurcation parameter can also be retrieved in a sub-window of time via a simple least-square-fit method.