Neural network approach to the TLS linear prediction frequency estimation problem

This paper presents a neural network approach to solving in real-time the linear prediction (LP) equation under the total least squares (TLS) criterion for the frequency estimation problem. We show both analytically and by simulations that this proposed neural network is guaranteed to be stable and to provide the results arbitrarily close to the accurate TLS solution of the LP equation within an elapsed time of only a few characteristic time constants of the circuit. The parameters such as the interconnection strengths of this proposed network can be obtained from the available data without any computation.