Fast signal subspace tracking using two-layer linear neural network learning

Abstract

In this paper, we present an orthonormal version of the new information criterion (NIC) algorithm for fast estimation and tracking of signal subspace using a twolayer linear neural network (NN). The fast orthonormal NIC referred as to FONIC algorithm guarantees the orthonormality of the weight matrix at each iteration. The proposed FONIC algorithm has a linear complexity which makes it efficient in real time applications. The FONIC algorithm provides a fast on-line learning of the optimum weights for the two-layer linear. Simulation results show better performance of FONIC algorithm than the NIC algorithm.Index Terms-new information criterion, subspace tracking, adaptive algorithm, neural network learning.I.

Fast Identification Algorithm of Time-varying Modal Parameter Based on Two-layer Linear Neural Network Learning

“…Different from PAST in cost function, NIC [21,22] (Novel Information Criterion) is proposed, adopting in two-layer linear neural network learning for subspace tracking. By introducing orthogonal method, a modified version is obtained named FONIC [23] . This paper presents a modified version of NIC adopting in two-layer linear neural network learning for subspace tracking.…”

Fast Time-varying modal parameter identification algorithm based on two-layer linear neural network learning for subspace tracking

“…Different from PAST in cost function, NIC [24,25] (Novel Information Criterion) is proposed, adopting in two-layer linear neural network learning for subspace tracking. By introducing orthogonal method, a modified version is obtained named FONIC [26] . Constructing Hankel matrix by input/output vectors respectively, we have:…”