The traditional cost function, minimization mean square prediction error is not a proper cost function in chaotic series prediction, for many chaotic signals are non-Gaussian distributions. Then we present using minimization error negentropy as new cost function, and derive the nonlinear approximation method. In simulation, the algorithm shows an enhanced performance to a common two order Volterra prediction.
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