System Identification Under Information Divergence Criteria

Chen, Badong; Zhu, Yu; Hu, Jinchun; Prı́ncipe, José C.

doi:10.1016/b978-0-12-404574-3.00005-1

Cited by 36 publications

(18 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, Gaussian assumption does not always hold in real-world environments, even though it is justified for many natural noises. When the signals are disturbed by non-Gaussian noises, the MSE based algorithms may perform poorly or encounter the instability problem [19], [23]. From a statistical viewpoint, the MSE is insufficient to capture all possible information in non-Gaussian signals.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, maximum correntropy criterion (MCC) has been successfully applied in diverse domains due to its simplicity and robustness [19], [23]- [29]. As a nonlinear and local similarity measure directly related to the probability of how similar two random variables are in the bisector neighborhood of the joint space controlled by the kernel bandwidth, correntropy is insensitive to large outliers, and is frequently used as a powerful method to handle non-Gaussian impulsive noises in various applications of engineering.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Constrained maximum correntropy adaptive filtering

et al. 2017

Self Cite

View full text Add to dashboard Cite

Abstract-Constrained adaptive filtering algorithms including constrained least mean square (CLMS), constrained affine projection (CAP) and constrained recursive least squares (CRLS) have been extensively studied in many applications. Most existing constrained adaptive filtering algorithms are developed under the mean square error (MSE) criterion, which is an ideal optimality criterion under Gaussian noises. This assumption however fails to model the behavior of non-Gaussian noises found in practice. Motivated by the robustness and simplicity of maximum correntropy criterion (MCC) for non-Gaussian impulsive noises, this paper proposes a new adaptive filtering algorithm called constrained maximum correntropy criterion (CMCC). Specifically, CMCC incorporates a linear constraint into a MCC filter to solve a constrained optimization problem explicitly. The proposed adaptive filtering algorithm is easy to implement and has low computational complexity, and in terms of convergence accuracy (say lower mean square deviation) and stability, it can significantly outperform those MSE based constrained adaptive algorithms in presence of heavy-tailed impulsive noises. Additionally, the mean square convergence behaviors are studied under energy conservation relation, and a sufficient condition to ensure the mean square convergence and the steady-state mean square deviation (MSD) of the proposed algorithm are obtained. Simulation results confirm the theoretical predictions under both Gaussian and non-Gaussian noises, and demonstrate the excellent performance of the novel algorithm by comparing it with other conventional methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Constrained maximum correntropy adaptive filtering

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…As a nonparametric method, the histogram-based estimation approach [23] is used for the PDF estimation in this paper. Suppose the i.i.d.…”

Section: Estimation Of the Performance Indexmentioning

confidence: 99%

Minimum Entropy-Based Cascade Control for Governing Hydroelectric Turbines

Ren

Zhang

et al. 2014

Entropy

View full text Add to dashboard Cite

Abstract:In this paper, an improved cascade control strategy is presented for hydroturbine speed governors. Different from traditional proportional-integral-derivative (PID) control and model predictive control (MPC) strategies, the performance index of the outer controller is constructed by integrating the entropy and mean value of the tracking error with the constraints on control energy. The inner controller is implemented by a proportional controller. Compared with the conventional PID-P and MPC-P cascade control methods, the proposed cascade control strategy can effectively decrease fluctuations of hydro-turbine speed under non-Gaussian disturbance conditions in practical hydropower plants. Simulation results show the advantages of the proposed cascade control method.

show abstract

“…The optimization criteria in information theoretic learning (ITL) [6] [7] have gained increasing attention over the past few years, which uses the information theoretic quantities (e.g. entropy) estimated directly from the data instead of the usual second order statistical measures, such as variance and covariance, as the optimization costs.…”

mentioning

confidence: 99%

“…In supervised learning, such as regression, the problem can be formulated as that of maximizing the correntropy between model output and desired response. This optimization criterion is called in ITL the maximum correntropy criterion (MCC) [6] [7]. Recently, the MCC has been successfully used in robust adaptive filtering in impulsive (heavy-tailed) noise environments [6] [9]-[11] [22].…”

mentioning

confidence: 99%