An analog architecture on parameter estimation of ARMA models

An analog architecture that is suitable for parameter estimation of autoregressive moving average (ARMA) models is proposed. The convergence theorem that connects this architecture with ARMA parameter estimation is presented. Simulation results indicate that its convergence takes only a few microsec...

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Bibliographic Details
Published inIEEE transactions on signal processing Vol. 41; no. 9; pp. 2946 - 2953
Main Authors Wu, W.-C., Chen, R.-M., Chang, S.
Format Journal Article
LanguageEnglish
Published New York, NY IEEE 01.09.1993
Institute of Electrical and Electronics Engineers
Subjects
Applied sciences
Computer architecture
Convergence
Detection, estimation, filtering, equalization, prediction
Entropy
Equations
Exact sciences and technology
Information, signal and communications theory
Least squares methods
Parameter estimation
Polynomials
Signal and communications theory
Signal processing
Signal processing algorithms
Signal, noise
Telecommunications and information theory
Very large scale integration
Autoregressive moving average
ARMA model
Parameter estimation
Algorithm
Hopfield model
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Summary:An analog architecture that is suitable for parameter estimation of autoregressive moving average (ARMA) models is proposed. The convergence theorem that connects this architecture with ARMA parameter estimation is presented. Simulation results indicate that its convergence takes only a few microseconds. Hence, this architecture can lead to online implementations.< >
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1053-587X
1941-0476
DOI:10.1109/78.236518