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natural gradient algorithm for neural networks applied to non-linear high power amplifiers

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 2002年第8期16卷 557-576页

作者： Abdulkader, H Langlet, F Roviras, D Castanie, F ENSEEIHT TeSA F-31071 Toulouse 7 France

This paper investigates the processing techniques for non-linear high power amplifiers (HPA) using neural networks (NNs). Several applications are presented: Identification and Predistortion of the HPA. Various Neural Network structures are proposed to identify and predistort the HPA. Since a few decades, NNs have shown excellent performance in solving complex problems (like classification, recognition, etc.) but usually they suffer from slow convergence speed. Here, we propose to use the natural gradient instead of the classical ordinary gradient in order to enhance the convergence properties. Results are presented concerning identification and predistortion using classical and natural gradient. Practical implementations issues are given at the end of the paper. Copyright (C) 2002 John Wiley Sons, Ltd.

关键词： high power amplifier identification predistortion equalization natural gradient algorithm neural networks

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natural gradient algorithm for stochastic distribution systems with output feedback

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DIFFERENTIAL GEOMETRY AND ITS APPLICATIONS 2013年第5期31卷 682-690页

作者： Zhang, Zhenning Sun, Huafei Peng, Linyu Beijing Inst Technol Dept Math Beijing 100124 Peoples R China Beijing Inst Technol Dept Math Beijing 100081 Peoples R China Univ Surrey Dept Math Guildford GU2 7XH Surrey England

In this paper, we use natural gradient algorithm to control the shape of the conditional output probability density function for the stochastic distribution systems from the viewpoint of information geometry. The considered system here is of multi-input and single output with an output feedback and a stochastic noise. Based on the assumption that the probability density function of the stochastic noise is known, we obtain the conditional output probability density function whose shape is only determined by the control input vector under the condition that the output feedback is known at any sample time. The set of all the conditional output probability density functions forms a statistical manifold (M), and the control input vector and the output feedback are considered as the coordinate system. The Kullback divergence acts as the distance between the conditional output probability density function and the target probability density function. Thus, an iterative formula for the control input vector is proposed in the sense of information geometry. Meanwhile, we consider the convergence of the presented algorithm. At last, an illustrative example is utilized to demonstrate the effectiveness of the algorithm. (c) 2013 Elsevier B.V. All rights reserved.

关键词： Stochastic distribution system Information geometry natural gradient algorithm Kullback divergence

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natural gradient algorithm for cyclostationary sources

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ELECTRONICS LETTERS 2002年第14期38卷 758-759页

作者： Jafari, MG Chambers, JA Mandic, DP Kings Coll London Ctr Digital Signal Proc Res London WC2R 2LS England Univ London Imperial Coll Sci & Technol Commun & Signal Proc Res Grp London England

A new approach to blind source separation of cyclostationary sources is introduced which incorporates a cyclic pre-whitening operation within the teaming rule, and thereby provides a new member of the family of natural gradient algorithms. The technique improves the convergence properties of the natural gradient algorithm for complex valued, cyclostationary signals. Simulations show the improved convergence speed of the approach.

关键词： signal sources signal processing convergence of numerical methods gradientmethods natural gradient algorithm blind source separation cyclic pre-whitening operation learning rule convergence properties cyclostationary signal

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natural gradient algorithm based on a class of activation functions and its applications in BSS

Natural gradient algorithm based on a class of activation fu...

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5th International Conference on Machine Learning and Cybernetics

作者： Li, Lei Wang, Yu Wang, Xing-Hui Nanjing Univ Posts & Telecommun Fac Math & Phys Nanjing 210003 Peoples R China

ISBN: (纸本)1424400600

Blind source separation has become a dominant domain of artificial neural network. It attempts to recover unknown independent sources from a given set of observed mixtures. The natural gradient algorithm is a very important approach for blind source separation (BSS). The selection of activation function is the key to the algorithm. The aim of this paper is to investigate the blind source separation of a linear mixture of independent communication signals by using the natural gradient algorithm. We compare various activation functions for the algorithm and propose a better one. Simulation results not only demonstrate the algorithm can effectively separate the two kinds of random mixing signals, but also show that the algorithm with proposed activation function converges faster than other activation functions.

关键词： blind source separation natural gradient algorithm activation function

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Knowledge-increasable artificial neural network and natural gradient algorithm

Knowledge-increasable artificial neural network and natural ...

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International Conference on Info-Tech and Info-Net (ICII 2001)

作者： Huang, YP Luo, SW Li, JY No Jiaotong Univ Dept Comp Sci & Technol Beijing Peoples R China

ISBN: (纸本)7900081585

This paper provides a novel knowledge-increasable artificial neural network model and learns parameters by using probability model. Conventional gradient algorithm is normally adopted to learn parameters in KI network, but its performance isn't the best This paper uses the natural gradient algorithm that takes the Riemannian metric of parameter space to define the parameters. This method can adaptively modify the parameters based on Riemannian metric and achieve the approximate best performance.

关键词： natural gradient algorithm knowledge-increasable artificial neural network

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natural gradient algorithm BASED ON A CLASS OF ACTIVATION FUNCTIONS AND ITS APPLICATIONS IN BSS

NATURAL GRADIENT ALGORITHM BASED ON A CLASS OF ACTIVATION FU...

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2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

作者： LEI LI YU WANG XING-HUI WANG Faculty of Mathematics and Physics Nanjing University of Post and Telecommunications Nanjing 210003 P.R.China

关键词： Blind source separation natural gradient algorithm activation function

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Blind Source Separation Based on Improved natural gradient algorithm

Blind Source Separation Based on Improved Natural Gradient A...

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8th World Congress on Intelligent Control and Automation (WCICA)

作者： Ce, Ji Peng, Yu Yang, Yu Northeastern Univ Coll Informat Sci & Engn Shenyang Liaoning Peoples R China

ISBN: (纸本)9781424467129

The natural gradient algorithm is the most basic independent component analysis (ICA) algorithm. Because the traditional natural gradient algorithm adopts fixed-step-size, the choice of step size directly affects the convergence speed and steadystate performance. This paper proposes an improved natural gradient algorithm by using the difference between the separation matrixes to control the factor of step size. The algorithm is a good solution to the trade-offs problems of convergence speed and steady-state performance. Meanwhile, the complexity of the algorithm is lower than the algorithm of reference [2] and reference [11]. The computer simulations have proved the effectiveness of the algorithm.

关键词： blind source separation adaptive step-size natural gradient algorithm

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Knowledge-increasable Artificial Neural Network and natural gradient algorithm

Knowledge-increasable Artificial Neural Network and Natural ...

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2001 International Conferences on Info-tech and Info-net

作者： Yaping Huang Siwei Luo Jianyu Li Department of Computer Science and Technology Northern Jiaotong University

This paper provides a novel knowledge-increasable artificial neural network model and learns parameters by using probability *** gradient algorithm is normally adopted to learn parameters in KI network,but its performance isn't the *** paper uses the natural gradient algorithm that takes the Riemannian metric of parameter space to define the *** method can adaptively modify the parameters based on Riemannian metric and achieve the approximate best performance.

关键词： natural gradient algorithm Knowledge-increasable Artificial Neural Network

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Scaled natural gradient algorithms for instantaneous and convolutive blind source separation

Scaled natural gradient algorithms for instantaneous and con...

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32nd IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Douglas, Scott C. Gupta, Malay So Methodist Univ Dept Elect Engn Dallas TX 75275 USA

ISBN: (纸本)1424407281

This paper describes a novel modification to the well-known natural gradient or INFOMAX algorithm for blind source separation that largely mitigates its divergence problems. The modified algorithm imposes an a posteriori scalar gradient constraint that adds little computational complexity to the algorithm and exhibits fast convergence and excellent performance for fixed step size values that are largely independent of input signal magnitudes and initial separation matrix estimates. Evaluation of the approach for the separation of instantaneous and convolutive source mixtures using both time-and frequency-domain implementations shows its excellent separation behavior.

关键词： blind source separation independent component analysis natural gradient algorithm

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Application of gradient descent algorithms based on geodesic distances

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Science China(Information Sciences) 2020年第5期63卷 176-186页

作者： Xiaomin DUAN Huafei SUN Linyu PENG School of Science Dalian Jiaotong University School of Mathematics and Statistics Beijing Institute of Technology Waseda Institute for Advanced Study Waseda University

In this paper, the Riemannian gradient algorithm and the natural gradient algorithm are applied to solve descent direction problems on the manifold of positive definite Hermitian matrices, where the geodesic distance is considered as the objective function. The first proposed problem is the control for positive definite Hermitian matrix systems whose outputs only depend on their inputs. The geodesic distance is adopted as the difference of the output matrix and the target matrix. The controller to adjust the input is obtained such that the output matrix is as close as possible to the target matrix. We show the trajectory of the control input on the manifold using the Riemannian gradient algorithm. The second application is to compute the Karcher mean of a finite set of given Toeplitz positive definite Hermitian matrices, which is defined as the minimizer of the sum of geodesic distances. To obtain more efficient iterative algorithm than traditional ones, a natural gradient algorithm is proposed to compute the Karcher mean. Illustrative simulations are provided to show the computational behavior of the proposed algorithms.

关键词： system control Riemannian gradient algorithm natural gradient algorithm Karcher mean Toeplitz positive definite Hermitian matrix

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