Online blind source separation method with adaptive step size in both time-invariant and time-varying cases

An adaptive step-size algorithm [Kushner and Yin,Stochastic Approximation and Recursive Algorithms and Applications, 2nd ed., New York: Springer-Verlag (2003)] is used to model time-varying learning, and its performance is illustrated in the environment of Marcet and Nicolini [American Economic Review93 (2003), 1476–1498]. The resulting model gives qualitatively similar results to those of Marcet and Nicolini, and performs quantitatively somewhat better, based on the criterion of mean squared error. The model generates increasing gain during hyperinflations and decreasing gain after hyperinflations end, which matches findings in the data. An agent using this model behaves cautiously when faced with sudden changes in policy, and is able to recognize a regime change after acquiring sufficient information.

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Adaptive Improved Natural Gradient Algorithm for Blind Source Separation

Neural Computation ◽

10.1162/neco.2008.07-07-562 ◽

2009 ◽

Vol 21 (3) ◽

pp. 872-889 ◽

Cited By ~ 10

Author(s):

Jian-Qiang Liu ◽

Da-Zheng Feng ◽

Wei-Wei Zhang

Keyword(s):

Blind Source Separation ◽

Source Separation ◽

Gradient Algorithm ◽

Expected Improvement ◽

Variable Step Size ◽

Time Varying ◽

Size Parameter ◽

Natural Gradient ◽

Step Size ◽

Simulation Results

We propose an adaptive improved natural gradient algorithm for blind separation of independent sources. First, inspired by the well-known backpropagation algorithm, we incorporate a momentum term into the natural gradient learning process to accelerate the convergence rate and improve the stability. Then an estimation function for the adaptation of the separation model is obtained to adaptively control a step-size parameter and a momentum factor. The proposed natural gradient algorithm with variable step-size parameter and variable momentum factor is therefore particularly well suited to blind source separation in a time-varying environment, such as an abruptly changing mixing matrix or signal power. The expected improvement in the convergence speed, stability, and tracking ability of the proposed algorithm is demonstrated by extensive simulation results in both time-invariant and time-varying environments. The ability of the proposed algorithm to separate extremely weak or badly scaled sources is also verified. In addition, simulation results show that the proposed algorithm is suitable for separating mixtures of many sources (e.g., the number of sources is 10) in the complete case.

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Time-Varying System Identification Using a Hybrid Blind Source Separation Method

Structural Health Monitoring, Damage Detection & Mechatronics, Volume 7 - Conference Proceedings of the Society for Experimental Mechanics Series ◽

10.1007/978-3-319-29956-3_11 ◽

2016 ◽

pp. 99-104 ◽

Cited By ~ 1

Author(s):

F. Musafere ◽

A. Sadhu ◽

K. Liu

Keyword(s):

System Identification ◽

Blind Source Separation ◽

Source Separation ◽

Separation Method ◽

Time Varying

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Adaptive Step Size Gradient Ascent ICA Algorithm for Wireless MIMO Systems

Mobile Information Systems ◽

10.1155/2018/7038531 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 4

Author(s):

Zahoor Uddin ◽

Ayaz Ahmad ◽

Muhammad Iqbal ◽

Zeeshan Kaleem

Keyword(s):

Computational Complexity ◽

Mimo Systems ◽

Separation Performance ◽

Time Varying ◽

Step Size ◽

Adaptive Step Size ◽

Gradient Ascent ◽

Wide Range ◽

Adaptive Step ◽

Size Gradient

Independent component analysis (ICA) is a technique of blind source separation (BSS) used for separation of the mixed received signals. ICA algorithms are classified into adaptive and batch algorithms. Adaptive algorithms perform well in time-varying scenario with high-computational complexity, while batch algorithms have better separation performance in quasistatic channels with low-computational complexity. Amongst batch algorithms, the gradient-based ICA algorithms perform well, but step size selection is critical in these algorithms. In this paper, an adaptive step size gradient ascent ICA (ASS-GAICA) algorithm is presented. The proposed algorithm is free from selection of the step size parameter with improved convergence and separation performance. Different performance evaluation criteria are used to verify the effectiveness of the proposed algorithm. Performance of the proposed algorithm is compared with the FastICA and optimum block adaptive ICA (OBAICA) algorithms for quasistatic and time-varying wireless channels. Simulation is performed over quadrature amplitude modulation (QAM) and binary phase shift keying (BPSK) signals. Results show that the proposed algorithm outperforms the FastICA and OBAICA algorithms for a wide range of signal-to-noise ratio (SNR) and input data block lengths.

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