Adaptive step-size natural gradient algorithm based on separating degree gradient

2006 ◽  
Author(s):  
Guangbiao Li ◽  
Shimin Xu
Keyword(s):  
Gradient Algorithm ◽  
Natural Gradient ◽  
Step Size ◽  
Adaptive Step Size ◽  
Adaptive Step

scholarly journals Analysis of Low Complexity Adaptive Step-size Orthogonal Gradient-based FEQ for OFDM Systems

1970 ◽  
Vol 5 (2) ◽  
pp. 134-145
Author(s):  
Suchada Sitjongsataporn
Keyword(s):  
Orthogonal Frequency Division Multiplexing ◽  
Weighted Least Squares ◽  
Low Complexity ◽  
Gradient Algorithm ◽  
Frequency Division ◽  
Ofdm Systems ◽  
Step Size ◽  
Adaptive Step Size ◽  
Gradient Based ◽  
Adaptive Step

We propose two low complexity adaptive step-size mechanisms based on the normalised orthogonal gradient algorithm for frequency-domain equalisation in orthogonal frequency division multiplexing (OFDM) systems. These algorithms are derived from employing a mixed-subcarrier exponentially weighted least squares criterion. Two low complexity adaptive stepsizeapproaches are investigated by exploiting an estimate of autocorrelation between previous and present weight-estimated mixed-subcarrier errors. We compare our approaches with a previously fixed stepsize normalised orthogonal gradient adaptive algorithm and other existing algorithm for implementation. Simulation results demonstrate that the proposed algorithms can achieve good performance for involving an OFDM receiver.

scholarly journals APPLICATION OF AN ADAPTIVE STEP-SIZE ALGORITHM IN MODELS OF HYPERINFLATION

2012 ◽  
Vol 16 (S3) ◽  
pp. 355-375 ◽  
Author(s):  
Olena Kostyshyna
Keyword(s):  
New York ◽  
Mean Squared Error ◽  
American Economic Review ◽  
Sufficient Information ◽  
Recursive Algorithms ◽  
Time Varying ◽  
Step Size ◽  
Adaptive Step Size ◽  
Squared Error ◽  
Adaptive Step

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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