Generalized iterative methods for enhancing contaminated chaotic signals

Generalized iterative methods for reducing noise in contaminated chaotic signals are proposed. These methods minimize a cost function composed of two parts: one containing information that represents how close enhanced signals are to the observed signal and another composed of constraints that fit the dynamics of the system. The convergence conditions and the error systems of the proposed methods are investigated. The proposed methods are compared via numerical experiments with Farmer's method which may be regarded as a special case of this general framework.