A coupled zeroing neural network for removing mixed noises in solving time-varying problems

Abstract

Harmonic noise frequently arouses by the disturbances in industrial applications, which would be a great threat to the security, stability and service life of equipment in some large and critical facilities, especially in power systems. Therefore, finding a way to resist harmonic noise is highly important. The zeroing neural networks (ZNN) have lately gained exceptional success in solving time-varying problems (TVP) as a result of its efficiency. Inspired by the effectiveness of ZNN and the dynamic system model design principles in control theory, we initially develop a coupled anti-mixed noise ZNN (AMNZNN) model that can resist the combination of single harmonic and non-harmonic noise (e.g., random noise). Then, an extended AMZNN model is further designed to remove the combination of multi-harmonic noise and non-harmonic noise. Additionally, comparisons among original ZNN (OZNN), integration-enhanced ZNN (IEZNN), harmonic-noise-tolerant ZNN (HNTZNN) and the proposed AMNZNN for time-varying matrix inversion (TVMI) under the mixture of harmonic noise and random noise are experimented to demonstrate the proposed AMNZNN model’s superior ability in resisting mixed noise. Finally, by applying the proposed extended formalism to power systems and microphone arrays in denoising, the effectiveness of the proposed method to resist multi-harmonic and random noises is further verified in scientific applications.