检索结果-内蒙古大学图书馆

Generalized predictive control of a class of MIMO models via a projection neural network

NEUROCOMPUTING 2017年 234卷 192-197页

作者： Ye, Qian Lou, Xuyang Sheng, Li Wuxi Inst Technol Wuxi 214121 Peoples R China Jiangnan Univ Inst Syst Engn Wuxi 214122 Peoples R China China Univ Petr East China Coll Informat & Control Engn Qingdao 266580 Peoples R China

The system identification and generalized predictive control of a class of multiple input multiple output models are studied. The generalized predictive control problem with unknown parameters is first addressed by finding a control sequence for control performance as a goal. Then, the unknown parameters of the models are estimated by a new stochastic gradient algorithm providing high estimation accuracy. Third, the generalized predictive control problem is formulated to a quadratic programming problem with linear inequality constraints. Finally, the constrained quadratic programming problem is solved through a generalized projection neural network with simple structure and small number of neurons, while previous projection neural networks have complex structure and require more neurons. Numerical simulations are provided to reinforce our theoretical results.

关键词： Generalized predictive control Projection neural network System identification stochastic gradient algorithm MIMO model

来源：评论

学校读者我要写书评

暂无评论

Parameter Estimation of a Class of Neural Systems with Limit Cycles

引用

algorithmS 2018年第11期11卷 169-169页

作者： Lou, Xuyang Cai, Xu Cui, Baotong Jiangnan Univ Inst Syst Engn 1800 Lihu Rd Wuxi 214122 Peoples R China

This work addresses parameter estimation of a class of neural systems with limit cycles. An identification model is formulated based on the discretized neural model. To estimate the parameter vector in the identification model, the recursive least-squares and stochastic gradient algorithms including their multi-innovation versions by introducing an innovation vector are proposed. The simulation results of the FitzHugh-Nagumo model indicate that the proposed algorithms perform according to the expected effectiveness.

关键词： parameter estimation neural system recursive least-squares algorithm stochastic gradient algorithm innovation

来源：评论

学校读者我要写书评

暂无评论

Convergence of the recursive identification algorithms for multivariate pseudo-linear regressive systems

引用

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 2016年第6期30卷 824-842页

作者： Wang, Xuehai Ding, Feng Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Peoples R China Henan Univ Urban Construct Sch Math & Phys Pingdingshan 467036 Peoples R China

The performance analysis of the recursive algorithms for the multivariate systems with an autoregressive moving average noise process is still open. This paper analyzes the convergence of two recursive identification algorithms, the multivariate recursive generalized extended least squares algorithm and the multivariate generalized extended stochastic gradient algorithm, for pseudo-linear multivariate systems and proves that the parameter estimation errors consistently converge to zero under persistent excitation conditions. The simulation results show that the proposed algorithms work well. Copyright (C) 2015 JohnWiley & Sons, Ltd.

关键词： parameter estimation least squares algorithm stochastic gradient algorithm pseudo-linear regressive model martingale convergence theorem

来源：评论

学校读者我要写书评

暂无评论

Performance analysis of the generalised projection identification for time-varying systems

引用

IET CONTROL THEORY AND APPLICATIONS 2016年第18期10卷 2506-2514页

作者： Ding, Feng Xu, Ling Zhu, Quanmin Jiangnan Univ Sch Internet Things Engn Minist Educ Key Lab Adv Proc Control Light Ind Wuxi 214122 Peoples R China Wuxi Vocat Inst Commerce Sch Internet Things Technol Wuxi 214153 Peoples R China Univ West England Dept Engn Design & Math Bristol BS16 1QY Avon England

The least mean square methods include two typical parameter estimation algorithms, which are the projection algorithm and the stochastic gradient algorithm, the former is sensitive to noise and the latter is not capable of tracking the time-varying parameters. On the basis of these two typical algorithms, this study presents a generalised projection identification algorithm (or a finite data window stochastic gradient identification algorithm) for time-varying systems and studies its convergence by using the stochastic process theory. The analysis indicates that the generalised projection algorithm can track the time-varying parameters and requires less computational effort compared with the forgetting factor recursive least squares algorithm. The way of choosing the data window length is stated so that the minimum parameter estimation error upper bound can be obtained. The numerical examples are provided.

关键词： time-varying systems least mean squares methods parameter estimation stochastic processes gradient methods performance analysis time-varying systems least mean square methods parameter estimation algorithms stochastic gradient algorithm time-varying parameters generalised projection identification algorithm stochastic process theory generalised projection algorithm forgetting factor recursive least squares algorithm data window length minimum parameter estimation error

来源：评论

学校读者我要写书评

暂无评论

A Fast stochastic gradient algorithm: Maximal Use of Sparsification Benefits under Computational Constraints

引用

IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2010年第2期E93A卷 467-475页

作者： Yukawa, Masahiro Utschick, Wolfgang RIKEN BSI Lab Math Neurosci Wako Saitama 3510198 Japan Tech Univ Munich Associate Inst Signal Proc D-80290 Munich Germany

In this paper, we propose a novel stochastic gradient algorithm for efficient adaptive filtering. The basic idea is to sparsity the initial error vector and maximize the benefits from the sparsification under computational constraints. To this end, we formulate the task of algorithm-design as a constrained optimization problem and derive its (non-trivial) closed-form solution. The computational constraints, are formed by focusing on the fact that the energy of the sparsified error vector concentrates at the first few components. The numerical examples demonstrate that the proposed algorithm achieves the convergence as fast as the computationally expensive method based on the optimization without the computational constraints.

关键词： adaptive filter stochastic gradient algorithm proportionate adaptive filtering

来源：评论

学校读者我要写书评

暂无评论

Differentially Coherent Multichannel Detection of Acoustic OFDM Signals

引用

IEEE JOURNAL OF OCEANIC ENGINEERING 2015年第2期40卷 251-268页

作者： Aval, Yashar M. Stojanovic, Milica Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA

In this paper, we propose a class of methods for compensating for the Doppler distortions of the underwater acoustic channel for differentially coherent detection of orthogonal frequency-division multiplexing (OFDM) signals. These methods are based on multiple fast Fourier transform (FFT) demodulation, and are implemented as partial (P), shaped (S), fractional (F), and Taylor (T) series expansion FFT demodulation. They replace the conventional FFT demodulation with a few FFTs and a combiner. The input to each FFT is a specific transformation of the input signal, and the combiner performs weighted summation of the FFT outputs. The four methods differ in the choice of the pre-FFT transformation (P, S, F, T), while the rest of the receiver remains identical across these methods. We design an adaptive algorithm of stochastic gradient type to learn the combiner weights for differentially coherent detection. The algorithm is cast into the multichannel framework to take advantage of spatial diversity. The receiver is also equipped with an improved synchronization technique for estimating the dominant Doppler shift and resampling the signal before demodulation. An additional technique of carrier sliding is introduced to aid in the post-FFT combining process when residual Doppler shift is nonnegligible. Synthetic data, as well as experimental data from a recent mobile acoustic communication experiment (few kilometers in shallow water, 10.5-15.5-kHz band) are used to demonstrate the performance of the proposed methods, showing significant improvement over conventional detection techniques with or without intercarrier interference equalization (5-7 dB on average over multiple hours), as well as improved bandwidth efficiency [ability to support up to 2048 quadrature phase-shift keying (QPSK) modulated carriers].

关键词： Differentially coherent detection Doppler fading intercarrier interference (ICI) mitigation orthogonal frequency-division multiplexing (OFDM) stochastic gradient algorithm synchronization underwater acoustic communication

来源：评论

学校读者我要写书评

暂无评论

Adaptive power allocation strategies for distributed space-time coding in cooperative MIMO networks

引用

IET COMMUNICATIONS 2014年第7期8卷 1141-1150页

作者： Peng, Tong de Lamare, Rodrigo C. Schmeink, Anke Univ York Dept Elect Commun Res Grp York YO10 5DD N Yorkshire England Rhein Westfal TH Aachen Inst Theoret Informat Technol D-52056 Aachen Germany

Adaptive power allocation (PA) algorithms with different criteria for a cooperative multiple-input multiple-output network equipped with distributed space-time coding are proposed and evaluated. Joint constrained optimisation algorithms to determine the PA parameters and the receive filter are proposed for each transmitted symbol in each link, as well as the channel coefficients matrix. Linear receive filter and maximum-likelihood detection are considered with amplify-and-forward and decode-and-forward cooperation strategies. In these proposed algorithms, the elements in the PA matrices are optimised at the destination node and then transmitted back to the relay nodes via a feedback channel. The effects of the feedback errors are considered. Linear minimum mean square error expressions and the PA matrices depend on each other and are updated iteratively. stochastic gradient algorithms are developed with reduced computational complexity. Simulation results show that the proposed algorithms obtain significant performance gains as compared with existing PA schemes.

关键词： channel allocation space-time codes cooperative communication MIMO communication channel coding filtering theory matrix algebra maximum likelihood detection amplify and forward communication decode and forward communication optimisation error statistics feedback stochastic processes gradient methods least mean squares methods adaptive power allocation strategy distributed space-time coding cooperative multiple input multiple output network joint constrained optimisation algorithm PA parameter determination symbol transmission channel coefflcients matrix linear receive filter maximum likelihood detection amplify and forward cooperation strategy decode and forward cooperation strategy destination node PA matrix optimisation relay nodes feedback channel feedback error linear minimum mean square error iterative method stochastic gradient algorithm computational complexity reduction

来源：评论

学校读者我要写书评

暂无评论

The iterative identification method for hammerstein nonlinear channel

The iterative identification method for hammerstein nonlinea...

引用

10th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM 2014)

作者： Rui Su Bin Wang Shigang Liu National Digital Switching System Engineering and Technological R&D Centre Zhengzhou China

ISBN: (纸本)9781510804166

This paper is concerned with the iteration identification algorithm for Hammerstein model with complex-valued input for the fact that the existing algorithms are not valid for complex input. Based on the stochastic gradient algorithm, the extended stochastic gradient algorithm is proposed by defining new cost function for complex input. The extended hierarchical multi-innovation stochastic gradient algorithm is proposed by introducing multi-innovation identification theory and hierarchical principle to the extended stochastic gradient algorithm. Experimental simulations show that the extended hierarchical multi-innovation stochastic gradient algorithm has better performance than the extended stochastic gradient algorithm at the expense of computational complexity.

关键词： Hammerstein Model Hierarchical Iterative Identification Multi-Innovation stochastic gradient algorithm Hierarchical stochastic algorithms nonlinear channel Identification methods Cost functions

来源：评论

学校读者我要写书评

暂无评论

Identification of nonlinear dynamic systems using convex combinations of multiple adaptive radius basis function networks

引用

MEASUREMENT 2013年第1期46卷 628-638页

作者： Zeng, Xiangping Zhao, Haiquan Jin, Weidong He, Zhengyou Li, Tianrui SW Jiaotong Univ Sch Informat Sci & Technol Chengdu 610031 Peoples R China SW Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China

To improve performance of nonlinear adaptive filter based on radius basis function (RBF) networks, a generalized combination scheme is proposed for nonlinear dynamic system identification in this paper. The nonlinear filter proposed is constructed by the convex combination of multiple RBF networks (MCRBF). Its adaptive algorithm with different step sizes is derived by the gradient descent rule, and can overcome the contradiction between convergence speed and precision of the stochastic gradient (SG) algorithm for RBF networks, which is imposed by the selection of a fixed value for the adaption step. Computer simulations demonstrate that the performance of the nonlinear filter proposed is superior to the RBF for nonlinear dynamic system identification in terms of convergence speed, steady state error and tracking capability. Crown Copyright (C) 2012 Published by Elsevier Ltd. All rights reserved.

关键词： Nonlinear dynamic system identification Radial basis function Convex combination stochastic gradient algorithm Nonlinear adaptive filter

来源：评论

学校读者我要写书评

暂无评论

stochastic gradient algorithm under (h,φ)-entropy criterion

引用

CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2007年第6期26卷 941-960页

作者： Chen, B. Hu, J. Pu, L. Sun, Z. Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China

Motivated by the work of Erdogmus and Principe, we use the error (h, phi)-entropy as the supervised adaptation criterion. Several properties of the (h, phi)-entropy criterion and the connections with traditional error criteria are investigated. By a kernel estimate approach, we obtain the nonparametric estimator of the instantaneous (h, phi)-entropy. Then, we develop the general stochastic information gradient algorithm, and derive the approximate upper bound for the step size in the adaptive linear neuron training. Moreover, the (h, phi) pair are optimized to improve the performance of the proposed algorithm. For the finite impulse response identification with white Gaussian input and noise, the exact optimum phi function is derived. Finally, simulation experiments verify the results and demonstrate the noticeable performance improvement that may be achieved by the optimum (h, phi)-entropy criterion.

关键词： (h, phi)-entropy stochastic gradient algorithm adaptive filtering kernel methods

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：