Parameter estimation of the exponentially damped sinusoids signal using a specific neural network

• We have proposed a new neural network method for estimating the parameter of the EDSs.
• We have constructed a specific topology strictly following the mathematic formulation of the EDSs.
• A modified LM algorithm is derived for iteratively solving the weights of EDSNN.

The problem of estimating the parameters of exponentially damped sinusoids (EDSs) signal has received very much attention in many fields. In this paper, a specific neural network termed EDSNN for parameter estimation of the EDSs has been proposed. Aiming at effectively evaluating the parameters of the EDSs signal, we construct a specific topology of EDSNN strictly following the mathematic formulation of EDSs signal. Then, what should be further done is how to train EDSNN using the data-set sampled from the EDSs signal. For this purpose, a modified Levenberg–Marquardt algorithm is derived for iteratively solving the weights of EDSNN by optimizing the pre-defined objective function. Profiting from good performance in fault tolerance of neural network, the proposed algorithm possesses a good performance in resistance to noise. Several computer simulations have been conducted to apply this method to some EDSs signal models. The results substantiate that the proposed EDSNN can synchronously obtain a higher precision for the damped factors, frequencies, also amplitudes and initial phases of all the EDSs than the state-of-the-art algorithm for noise free or noise case.