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10th International Conference on Advanced Computational Intelligence (ICACI)

作者： Sun, Qitang Dang, Lujuan Wang, Wanli Wang, Shiyuan Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China

ISBN: (纸本)9781538643624

This paper presents a novel kernel least square algorithm with mixed kernel (KLMS-MK) to improve the filtering performance of kernel least mean square (KLMS). By applying the convex combination method to the kernel function in KLMS, KLMS-MK bears the advantages of both the Gaussian kernel and the Laplace kernel. In KLMS-MK, the mixed parameter for the convex combination is updated with the stochastic gradient descent. Therefore, the steady-state mean square error (MSE) and the convergence rate are improved by KLMS-MK, simultaneously. Simulation results on chaotic time series prediction and nonlinear regression validate the excellent performance of KLMS-MK from the aspects of the convergence rate and estimation accuracy.

关键词： kernel least mean square algorithm Gaussian kernel Laplace kernel mixed parameter

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Gaussian kernel least mean square algorithm With Improved Novelty Criterion 34

Gaussian Kernel Least Mean Square Algorithm With Improved No...

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34th Youth Academic Annual Conference of Chinese-Association-of-Automation (YAC)

作者： Wang, Fuping Su, Yixin Leng, Zhiwen Qi, Yue Wuhan Univ Technol Sch Automat Wuhan Peoples R China China Ship Dev & Design Ctr Wuhan Peoples R China

ISBN: (纸本)9781728139364

The kernel-based adaptive algorithm has been widely applied to noise cancellation, but the computational complexity of kernel function causes it can't perform well in embedded real-time control system. This paper proposes a design of the Gaussian kernel least mean square algorithm with improved novelty criterion, which aims to reduce computational load with universal approximation and fast convergence speed. The methodology slows down the growth rate of the network by multiple operations on the training set. It finds out the filter parameter from the collection data by improved novelty criterion, so the filter network has a much smaller scale and computation complexity, which allow it to be used in the embedded real-time control system.

关键词： Gaussian kernel kernel least mean square algorithm improved novelty criterion nonlinear system ship degaussing system

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Nonlinear backward ADPCM speech coding using kernel methods and online sparsification

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TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES 2014年第2期25卷 161-172页

作者： Alipoor, Ghasem Savoji, Mohammad Hassan Shahid Beheshti Univ Fac Elect & Comp Engn Tehran *** Iran

Modeling using neural networks and Volterra expansion are widely used in signal processing to account for nonlinear characteristics inherent in natural signals. In spite of their good performance in a wide range of applications, adaptive implementations of these models suffer from severe problems of a large number of parameters, slow convergence, and the risk of being trapped in local minima. Furthermore, inherent instability of the Volterra filters makes them unsuitable for applications that require synthesis or reconstruction, for example, speech coding. Nonetheless, kernel methods in the framework of reproducing kernel Hilbert spaces are emerging solutions that can tackle these problems by extending linear algorithms to spaces created by nonlinear mapping of the input signal. In this paper, we address the problems encountered in nonlinear adaptive coding by employing, for the first time, several kernel adaptive algorithms in the framework of speech coding using the adaptive differential pulse code modulation technique. Particular attention is focused on the effects of various sparsification techniques on the performance of the resultant algorithms. Simulation results show that utilizing kernel methods results in an average improvement of up to 3.4 dB in the signal-to-noise ratio of the reconstructed speech. This is while a lower improvement of up to 1.4 dB is achieved by exploiting Volterra filters at the cost of a demanding increase in the complexity, a slight increase in the bit-rate and introducing a relatively long delay. Copyright (c) 2012 John Wiley & Sons, Ltd.

关键词： kernel methods kernel adaptive filtering algorithms kernel least mean square algorithm kernel least-squares algorithms speech coding adaptive differential pulse code modulation

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kernel Adaptive Filtering with Maximum Correntropy Criterion

Kernel Adaptive Filtering with Maximum Correntropy Criterion

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhao, Songlin Chen, Badong Principe, Jose C. Univ Florida Dept Elect & Comp Engn Gainesville FL 32610 USA

ISBN: (纸本)9781424496365

kernel adaptive filters have drawn increasing attention due to their advantages such as universal nonlinear approximation with universal kernels, linearity and convexity in Reproducing kernel Hilbert Space (RKHS). Among them, the kernel least mean square (KLMS) algorithm deserves particular attention because of its simplicity and sequential learning approach. Similar to most conventional adaptive filtering algorithms, the KLMS adopts the mean square error (MSE) as the adaptation cost. However, the mere second-order statistics is often not suitable for nonlinear and non-Gaussian situations. Therefore, various non-MSE criteria, which involve higher-order statistics, have received an increasing interest. Recently, the correntropy, as an alternative of MSE, has been successfully used in nonlinear and non-Gaussian signal processing and machine learning domains. This fact motivates us in this paper to develop a new kernel adaptive algorithm, called the kernel maximum correntropy (KMC), which combines the advantages of the KLMS and maximum correntropy criterion (MCC). We also study its convergence and self-regularization properties by using the energy conservation relation. The superior performance of the new algorithm has been demonstrated by simulation experiments in the noisy frequency doubling problem.

关键词： Approximation algorithms Approximation methods Cost function Hilbert spaces kernel Noise Random variables Robustness adaptive filters higher-order statistics kernel adaptive filtering kernel least mean square algorithm kernel maximum correntropy least mean squares methods maximum correntropy criterion maximum entropy methods mean square error reproducing kernel Hilbert space second-order statistics sequential learning approach universal kernels universal nonlinear approximation

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A Nonparametric Information Theoretic Approach for Change Detection in Time Series

A Nonparametric Information Theoretic Approach for Change De...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhao, Songlin Principe, Jose C. Univ Florida Dept Elect & Comp Engn Gainesville FL 32610 USA

ISBN: (纸本)9781424496365

This paper presents an online nonparametric methodology based on the kernel least mean square (KLMS) algorithm and the surprise criterion, which is based on an information theoretic framework. Surprise quantifies the amount of information a datum contains given a known system state, and can be estimated online using Gaussian Process Theory. Based on this concept, we use the KLMS algorithm together with surprise criterion to detect regime change in nonstationary time series. We test the methodology on a synthesized chaotic time series to illustrate this criterion. The results show that surprise criterion is better than the conventional segmentation based on the error criterion.

关键词： Computational modeling Data models Gaussian process theory Gaussian processes Gaussian processes KLMS algorithm kernel Prediction algorithms Time series analysis Training change detection chaotic time series error criterion information theory kernel least mean square algorithm least mean squares methods nonparametric information theoretic approach nonstationary time series online nonparametric methodology surprise criterion time series

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