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15th IEEE International Symposium on Personal, Indoor, Mobile, Radio Communications (PIMRC 2004)

作者： Yiu, S Schober, R Gerstacker, W Univ British Columbia Vancouver BC V5Z 1M9 Canada

ISBN: (纸本)0780385233

In this paper, we optimize delay diversity (DD) for the case when computationally efficient decision-feedback equalization (DFE) is used at the receiver. We consider the general case of transmission over a cor-related multiple-input multiple-output (MIMO) frequency-selective fading channel. The proposed optimization requires the knowledge of the statistical properties of the wireless channel at the transmitter, but channel state information is only required at the receiver side. Based on an approximation of the pairwise error probability for DFE, we derive a stochastic gradient algorithm that allows us to optimize the DD transmit filters. Simulation results for the GSM/EDGE system show significant performance gains of the proposed optimized DD scheme over the DD schemes reported in [I] and [2], respectively, if DFE is used at the receiver.

关键词： delay diversity decision-feedback equalization frequency-selective fading channel multiple-input multiple-output systems stochastic gradient algorithm

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Parameter identification of Volterra nonlinear system based on Levenberg-Marquardt recursive algorithm 34

Parameter identification of Volterra nonlinear system based ...

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34th Chinese Control and Decision Conference (CCDC)

作者： Chu, Jie Li, Junhong Zong, Tiancheng Nantong Univ Sch Elect Engn Nantong 226019 Peoples R China

ISBN: (纸本)9781665478960

The Volterra model can approximate many nonlinear systems, and it is a typical nonlinear system. This paper studies the parameter estimation problem of the Volterra model. Combining the Levenberg-Marquardt optimization method and the recursive identification method, we propose a Levenberg-Marquardt recursive algorithm and apply it to the identification of the Volterra system. In order to verify the feasibility of the above algorithm, the second-order Volterra system is simulated using the Levenberg-Marquardt recursive algorithm and the forgetting factor stochastic gradient algorithm respectively, and then we compare the simulation results of the Volterra system under the two algorithms. The simulation results show that the above two algorithms can identify the parameters of the Volterra system. Compared with the forgetting factor stochastic gradient algorithm, the Levenberg- Marquardt recursive algorithm has faster convergence speed and higher convergence accuracy. This proves the effectiveness of the Levenberg-Marquardt recursive algorithm.

关键词： parameter estimation nonlinear systems Volterra model Levenberg-Marquardt recursive algorithm stochastic gradient algorithm

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Neural Network Training Method Based on Dynamic Segmentation of the Training Dataset 39

Neural Network Training Method Based on Dynamic Segmentation...

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39th Youth Academic Annual Conference of Chinese-Association-of-Automation (YAC)

作者： Yang, Pengfei China Elect Technol Grp Corp Res Inst 27 Zhengzhou Peoples R China

ISBN: (纸本)9798350390780;9798350379228

We propose a neural network training method based on the dynamic partition of the training data set. The main idea is to perform multiple iterative training on the neural network model. Each iteration of training splits the training data set, and allows the neural network model to train independently on several sub datasets obtained by the segmentation. According to the error performance on the entire training data set of the neural network model trained on the sub-dataset, the optimal neural network model with the smallest error is selected as the initial network for the next iteration *** show that this training method based on dynamic segmentation of the training data set has two advantages compared with the traditional training method. One is to improve the training efficiency, because the sub-datasets obtained by each segmentation are independent of each other. Data set segmentation can be used for parallel training to accelerate training. The other is that this progressive training model of training data sets from small to large, from less to more, conforms to the changing law of human understanding of things, and makes the model have better training accuracy and generalization ability.

关键词： dataset segmentation progressive expansion iterative training stochastic gradient algorithm generalization ability

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MIMO Transmission with Rank Adaptation for Multi-Gigabit 60GHz Wireless

MIMO Transmission with Rank Adaptation for Multi-Gigabit 60G...

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IEEE Global Telecommunications Conference (GLOBECOM 2010)

作者： Jiang, Meilong Yue, Guosen Rangarajan, Sampath NEC Laboratories America Inc. United States

ISBN: (纸本)9781424456383

In this paper, we propose a practical and systematic approach to implement the MIMO transmission with rank adaptation for 60 GHz systems. In the 60 GHz system with multiple antennas, the transmit and receive (Tx-Rx) antenna arrays are grouped into a number of subarrays with a predetermined subarray separation based on the derived geometrical criteria of creating high rank MIMO in LoS environments. We first apply an enhanced blind beamforming technique based on a stochastic gradient algorithm (SGA) for the inner-subarray antennas, which does not require channel state information (CSI) at either the transmitter or the receiver. Secondly, the composite MIMO channel, as a joint effect of Tx-Rx beamforming and the channel impulse response, can be estimated with much reduced complexity. Finally, the MIMO transmission with rank adaptation is performed by adaptively selecting the better scheme out of the high-rank spatial multiplexing and the rank-1 beamforming whichever gives a higher system throughput. Simulation results show that high-rank spatial multiplexing and rank-1 beamforming outperform each other at different geometrical placements and transmit power settings. The proposed MIMO transmission with rank adaptation offers significant performance gain especially at high signal-to-noise ratio (SNR) regions.

关键词： MIMO communication MIMO transmission antenna arrays frequency 60 GHz multi-gigabit wireless multiple antennas radio networks rank adaptation receiving antennas stochastic gradient algorithm transmit and receive antenna arrays transmitting antennas

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A new identification method for dual-rate Wiener systems

A new identification method for dual-rate Wiener systems

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2nd International Conference on Advanced Design and Manufacturing Engineering (ADME 2012)

作者： Zhang, Yan Chen, Jing Wuxi Inst Technol Wuxi 214121 Peoples R China

ISBN: (纸本)9783037855034

A new parameter estimation method is introduced for dual-rate Wiener systems. The method uses the finite input response model and a stochastic gradient algorithm to estimate the parameters of the finite input response model. The origin parameters can be computed by the estimated parameters. Simulation studies of dual-rate Wiener systems identification are included.

关键词： Estimation method FIR model stochastic gradient algorithm Wiener system

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On Large Batch Training and Sharp Minima: A Fokker-Planck Perspective

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JOURNAL OF STATISTICAL THEORY AND PRACTICE 2020年第3期14卷 1-31页

作者： Dai, Xiaowu Zhu, Yuhua Univ Calif Berkeley CDAR Berkeley CA 94720 USA Univ Calif Berkeley Simons Inst Theory Comp Berkeley CA 94720 USA Stanford Univ Dept Math Stanford CA 94305 USA

We study the statistical properties of the dynamic trajectory of stochastic gradient descent (SGD). We approximate the mini-batch SGD and the momentum SGD as stochastic differential equations. We exploit the continuous formulation of SDE and the theory of Fokker-Planck equations to develop new results on the escaping phenomenon and the relationship with large batch and sharp minima. In particular, we find that the stochastic process solution tends to converge to flatter minima regardless of the batch size in the asymptotic regime. However, the convergence rate is rigorously proven to depend on the batch size. These results are validated empirically with various datasets and models.

关键词： Large batch training Sharp minima Fokker-Planck equation stochastic gradient algorithm Deep neural network

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Adaptive Filtering for Desired Error Distribution under Minimum Information Divergence Criterion

Adaptive Filtering for Desired Error Distribution under Mini...

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International Joint Conference on Neural Networks

作者： Hu, Jinchun Chen, Badong Sun, Fuchun Sun, Zengqi Tsinghua Univ State Key Lab Intelligent Technol & Syst Dept Comp Sci & Technol Beijing 100084 Peoples R China

ISBN: (纸本)9781424418206

Conventional cost functions of adaptive filtering are usually related to the error's dispersion, such as error's moments or error's entropy, but neglect the shape aspects (peaks, kurtosis, tails, etc.) of the error distribution. In this work, we propose a new notion of filtering (or estimation) in which the error's probability density function (PDF) is shaped into a desired one. As PDFs contain all the probabilistic information, the proposed method can be used to achieve the desired error variance or error entropy, and is expected to be useful in the complex signal processing and learning systems. In our approach, the information divergence between the actual errors and the desired errors is used as the cost function. By kernel density estimation, we derive the associated stochastic gradient algorithm for the finite impulse response (FIR) filter. Simulation results emphasize the effectiveness of this new algorithm in adaptive system training.

关键词： Adaptive filtering Information divergence stochastic gradient algorithm Kernel density estimation

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Linear Dispersion Codes Design for Asynchronous Cooperative Communications

Linear Dispersion Codes Design for Asynchronous Cooperative ...

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22nd IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Wang, Wenjin Zheng, Fu-Chun Univ Reading Sch Syst Engn Reading RG6 6AY Berks England

ISBN: (纸本)9781457713484

In this paper, we study the design of linear dispersion codes (LDCs) for asynchronous cooperative communication networks. Firstly, the concept of conventional LDCs is extended to the delay-tolerant version and new design criteria are discussed. Then we propose a new design method to yield delay-tolerant LDCs that approach near-optimal capacity as well as minimum average pairwise error probability. The proposed design employs stochastic gradient algorithm to guarantee the local optimum. Moreover, it is improved by using simulated annealing type optimization to approach the global optimum. The proposed method allows flexible number of nodes, receive antennas, the length of codewords and the number of modulated symbols. Simulation results confirm the performance of the newly-proposed delay-tolerant LDCs.

关键词： Delay Dispersion Error probability Optimization Receiving antennas Relays Signal to noise ratio asynchronous cooperative communications cooperative communication delay-tolerant version error statistics linear codes linear dispersion codes design minimum average pairwise error probability receive antennas receiving antennas simulated annealing stochastic gradient algorithm

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Fast Distributed stochastic Scheduling for A Multi-Energy Industrial Park 4

Fast Distributed Stochastic Scheduling for A Multi-Energy In...

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4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS)

作者： Zhu, Dafeng Yang, Bo Wang, Zhaojian Ma, Chengbin Ma, Kai Zhu, Shanying Shanghai Jiao Tong Univ Dept Automat Shanghai Peoples R China Shanghai Jiao Tong Univ Univ Michigan Shanghai Jiao Tong Univ Joint Inst Shanghai Peoples R China Yanshan Univ Sch Elect Engn Qinhuangdao Hebei Peoples R China

ISBN: (纸本)9781728162072

The multi-energy management framework of industrial parks advocates energy conversion and scheduling, which takes full advantage of the compensation and temporal availability of multiple energy. However, how to exploit elastic loads and compensate inelastic loads to match multiple generators and storage is still a key problem under the uncertainty of demand and supply. To solve the issue, the energy management problem is constructed as a stochastic optimization problem. The optimization aims are to minimize the time-averaged energy cost and improve the energy efficiency while respecting the energy constraints. To achieve the distributed implementation in real time without knowing any priori knowledge of underlying stochastic process, a distributed stochastic gradient algorithm based on dual decomposition and a fast scheme are proposed. The numerical results based on real data show that the industrial park, by adopting the proposed algorithm, can achieve social welfare maximization asymptotically.

关键词： Multi-energy management framework industrial park distributed implementation stochastic gradient algorithm

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Study on Sparse Blinding Separation Based on the Linear Detection and STFT

Study on Sparse Blinding Separation Based on the Linear Dete...

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International Conference on Graphic and Image Processing (ICGIP)

作者： Yang Hui-cheng Wang Xiao-xue Anhui Polytech Univ Coll Elect Engn Wuhu Peoples R China

ISBN: (纸本)9780819489326

This topic focuses on the underdetermined sparse blinding source separation from the mixtures. A novel algorithm is put forwards based on the linear direction detection and short-time Fourier transformation (STFT). This algorithm includes two stages: (1) the mixing matrix is estimated in the frequency domain by the STFT and a stochastic gradient algorithm;and (2) the linear programming method is applied to separate the source from the mixtures in the frequency and then the frequency domain source is transformed into the time domain source by the inverse STFT. Finally, some experimental results show that the algorithm is effective.

关键词： Sparse blinding separation undetermined the mixing matrix Sparse source stochastic gradient algorithm STFT

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