Hierarchical maximum likelihood generalized extended stochastic gradient algorithms for bilinear‐in‐parameter systems
Copyright policy )Hierarchical maximum likelihood generalized extended stochastic gradient algorithms for bilinear‐in‐parameter systems
AbstractIn this article, we use the maximum likelihood principle and the multi‐innovation identification theory to study the identification issue of a bilinear‐in‐parameter system with autoregressive moving average noise. A maximum likelihood multi‐innovation stochastic gradient algorithm is derived to estimate the model parameters, which uses not only the current innovation but also the past innovations to improve the parameter estimation accuracy. The maximum likelihood multi‐innovation stochastic gradient algorithm has higher parameter estimation accuracy than the stochastic gradient algorithm. The simulation examples indicate that the proposed methods work well.
- Qingdao University of Science and Technology China (People's Republic of)
- Qingdao University China (People's Republic of)
gradient search, Estimation and detection in stochastic control theory, Identification in stochastic control theory, multi-innovation identification, Nonlinear systems in control theory, maximum likelihood, nonlinear system, parameter estimation
gradient search, Estimation and detection in stochastic control theory, Identification in stochastic control theory, multi-innovation identification, Nonlinear systems in control theory, maximum likelihood, nonlinear system, parameter estimation
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).23 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
Citations provided by BIP!Popularity provided by BIP!