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Logarithmic Hyperbolic Cosine Adaptive Filter and Its Performance Analysis

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2021年第4期51卷 2512-2524页

作者： Wang, Shiyuan Wang, Wenyue Xiong, Kui Iu, Herbert H. C. Tse, Chi K. Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Univ Western Australia Sch Elect Elect & Comp Engn Crawley WA 6009 Australia Hong Kong Polytech Univ Dept Elect & Informat Engn Hong Kong Peoples R China

The hyperbolic cosine function with high-order errors can be utilized to improve the accuracy of adaptive filters. However, when initial weight errors are large, the hyperbolic cosine-based adaptive filter (HCAF) may be unstable. In this paper, a novel normalization based on the logarithmic hyperbolic cosine function is proposed to achieve the stabilization for the case of large initial weight errors, which generates a logarithmic HCAF (LHCAF). Actually, the cost function of LHCAF is the logarithmic hyperbolic cosine function that is robust to large errors and smooth to small errors. The transient and steady-state analyses of LHCAF in terms of the mean-square deviation (MSD) are performed for a stationary white input with an even probability density function in a stationary zero-mean white noise. The convergence and stability of LHCAF can be therefore guaranteed as long as the filtering parameters satisfy certain conditions. The theoretical results based on the MSD are supported by the simulations. In addition, a variable scaling factor and step-size LHCAF (VSS-LHCAF) is proposed to improve the filtering accuracy of LHCAF further. The proposed LHCAF and VSS-LHCAF are superior to HCAF and other robust adaptive filters in terms of filtering accuracy and stability.

关键词： Cost function Robustness Performance analysis Convergence Steady-state Adaptive systems Stability analysis Convergence hyperbolic function robust adaptive filter stability steady-state analysis

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Distributed Neuro-Dynamic Algorithm for Price-Based Game in Energy Consumption System

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NEURAL PROCESSING LETTERS 2020年第1期51卷 559-575页

作者： Wen, Shifan He, Xing Huang, Tingwen Southwest Univ Sch Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Texas A&M Univ Qatar Doha 5825 Qatar

In this paper, a plug-in hybrid electric vehicles energy consumption system is studied. In order to protect each player's privacy, the information exchange is going on the neighboring players, and a connected undirected graph is used to pattern the information flow between the players. Hence, it is impossible for each player to access the aggregate electricity consumption directly, which determines the electricity price. Under the noncooperative game frame, a distributed neuro-dynamic algorithm is proposed to optimize the benefit of each individual player base on the pricing strategies. A dynamic average consensus is applied to estimate the aggregate consumption and a projection neural network is employed to seek the Nash equilibrium point. The convergence of the proposed distributed algorithm is analyzed through the Lyapunov stability analysis. Finally, the effectiveness of the distributed neuro-dynamic algorithm is manifested in the simulation.

关键词： Distributed neuro-dynamic algorithm Dynamic average consensus Projection neural network Noncooperative game Nash equilibrium

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Robust Multikernel Maximum Correntropy Filters

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IEEE TRANSACTIONS ON circuits AND SYSTEMS II-EXPRESS BRIEFS 2020年第6期67卷 1159-1163页

作者： Xiong, Kui Shi, Wei Wang, Shiyuan Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China

The multikernel adaptive filters based on the minimum mean square error (MMSE) criterion have been proposed to improve the performance of the kernel least mean square (KLMS), efficiently. However, these multikernel methods suffer from large computational burden as well as instability in impulsive noises. To address these issues, a novel multikernel method is proposed by replacing the trace operation of a matrix with the inner product of two vectors, thus leading to higher computational efficiency, significantly. Based on the maximum correntropy criterion, the multikernel maximum correntropy (MKMC) algorithm is therefore proposed. To further reduce the complexity, an online vector quantization strategy is presented for MKMC to generate the quantized MKMC (QMKMC) algorithm. Monte Carlo simulations on different nonlinear examples validate the superiorities of the proposed two algorithms.

关键词： Kernel Noise measurement Bandwidth Artificial neural networks Convergence Estimation error Dictionaries Multikernel algorithm maximum correntropy criterion robust adaptive filter vector quantization

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Complex-Valued Adaptive Filtering Based on the Random Fourier Features Method

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IEEE TRANSACTIONS ON circuits AND SYSTEMS II-EXPRESS BRIEFS 2020年第10期67卷 2284-2288页

作者： Xiong, Kui Zhang, Yu Wang, Shiyuan Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Southwest Univ Fac Comp & Informat Sci Chongqing 400715 Peoples R China

The complex kernel adaptive filters (CKAFs) developed in complex reproducing kernel Hilbert space (RKHS) improve the performance of complex linear adaptive filters, but result in large burdens of computation and memory. To address these issues, a novel complex random Fourier features mapping (CRFFM) is proposed to approximate the kernel-induced mapping by applying the complexification of real RKHSs, and thus projects the complex-valued data from the original data space into the fixed-dimensional complex random Fourier features space (RFFS). To combat complex-valued non-Gaussian noises, a complex Cauchy loss function is presented, and the complex random Fourier features recursive complex Cauchy (CRFFRCC) algorithm is therefore proposed by the combination of CRFFM and the stochastic recursive method. The proposed CRFFRCC with a linear filter structure can reduce the computational and space complexities of CKAFs, significantly. Monte Carlo simulations conducted in the complex-valued nonlinear channel equalization validate the superiorities of CRFFRCC.

关键词： Kernel Signal processing algorithms Complexity theory Adaptive systems Probability density function Filtering Complex kernel adaptive filter random Fourier features complex Cauchy loss recursive method complexity

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Time Domain Analysis of 3-D Microwave Film Bulk Acoustic Resonator Based on Electro-Mechanical Coupling Algorithm

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IEEE MICROWAVE AND WIRELESS COMPONENTS LETTERS 2022年第5期32卷 367-370页

作者： Chen, Shitao Yin, Qiupeng Wang, Wei Chen, Ming Xuan, Weipeng Huang, Zhixiang Yu, Ming Anhui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230093 Peoples R China Anhui Univ Anhui Prov Key Lab Target Recognit & Feature Extr Hefei 230093 Peoples R China China Elect Technol Grp Corp Res Inst 32 Shanghai 200233 Peoples R China East China Res Inst Elect Engn Hefei 230088 Peoples R China Hangzhou Dianzi Univ Coll Elect & Informat Minist Educ Key Lab RF Circuits & Syst Hangzhou 310005 Peoples R China Southern Univ Sci & Technol SUSTech Dept Elect & Elect Engn Shenzhen 230093 Peoples R China

This letter presents an electro-mechanical coupling model for multiphysical field analysis of 3-D microwave film bulk acoustic resonator (FBAR). Microwave FBAR is composed of metal electrode, piezoelectric materials, and substrate. The electrical and mechanical characteristic are analyzed by three equations of piezoelectric constitutive equation, electrostatic field, and Newtonian equations of motion, respectively. Multiphysical coupling model equation is constructed by establishing the relationship between two equations through common physical quantities potential and displacement. In the end, the multiphysical coupling model equation is calculated using the finite-difference time-domain (FDTD) method. Compared with the commercial software (COMSOL), the accuracy of the proposed electro-mechanical method is verified while the computational efficiency and memory requirements are significantly reduced.

关键词： Film bulk acoustic resonators Mathematical models Finite difference methods Couplings Time-domain analysis Microwave theory and techniques Electrodes Electro-mechanical coupling model finite-difference time-domain (FDTD) method microwave film bulk acoustic resonator (FBAR) multiphysical analysis

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Robust adaptive filtering with variable risk-sensitive parameter and kernel width

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ELECTRONICS LETTERS 2020年第15期56卷 791-792页

作者： Liu, Yingzhi Dong, Fei Yu, Xin Qian, Guobing Wang, Shiyuan Southwest Univ Westa Coll Chongqing Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing Peoples R China

Similarity measures play a significant role in adaptive filtering. Previous work such as correntropy and kernel risk-sensitive loss (KRSL), has successfully improved the technology of adaptive filtering in terms of robustness against outliers, fast convergence speed and high filtering accuracy. Based on KRSL, a newly raised similarity measure, complex KRSL (CKRSL), was proposed by extending KRSL to the complex domain. It successfully gains superior performance than other similarity measures in adaptive filtering algorithms. However, the minimum CKRSL (MCKRSL) algorithm may result in poor performance when the parameters are not properly chosen. In this Letter, an adaptive parameter selection is proposed to help the MCKRSL algorithm improve performance while overcoming the uncertainty in artificial selection. The proposed MCKRSL with variable parameters (MCKRSL-VP) algorithm updates the risk-sensitive parameter and kernel width by making the iteratively squared bias as small as possible. A moving average scheme is further used to smoothly update the risk-sensitive parameter and kernel width. Finally, the authors verify that MCKRSL-VP performs better than other algorithms by simulations.

关键词： adaptive filters estimation theory iterative methods mean square error methods adaptive parameter selection kernel width robust adaptive filtering variable risk-sensitive parameter correntropy kernel risk-sensitive loss similarity measure complex KRSL minimum CKRSL algorithm MCKRSL-VP KRSL

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Reservoir computing based encryption-then-compression scheme of image achieving lossless compression

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 256卷

作者： Jiang, Xiao Xie, Yiyuan Zhang, Yushu Gulliver, Thomas Aaron Ye, Yichen Xu, Fang Yang, Yandong Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Univ Chongqing Key Lab Networks & Cloud Comp Secur Chongqing 400715 Peoples R China Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China Univ Victoria Dept Elect & Comp Engn Victoria BC V8W 2Y2 Canada IEI AI & HPC Software Dept Beijing 100080 Peoples R China

Image encryption-then-compression (ETC), combining encryption and compression techniques, is a powerful strategy for image privacy protection. One of the most significant concerns in ETC is to realize a trade-off between high compression and high-quality recovery. To this end, we propose a novel ETC scheme based on a ring-network optical dynamic system and a reservoir computing (RC) system. This optical dynamics system is newly constructed by using three QD spin-VCSELs, then the optical RC system is further specifically designed. To ensure security, we develop a secret key generation method, which involves jointly injecting the original image and chaotic data into the RC system for chaotic transformation to produce high-security secret keys. In our scheme, the original image is first encrypted through scrambling and diffusion using the generated keys, then the obtained encrypted image is segmented into multiple sub-blocks, followed by using uniform down-sampling to select data, the unsampled data will be discarded and the sampled data is to be retained as the resulting compressed image. Flexible compression ratio can be achieved by varying sub-block sizes, with larger sub-blocks yielding higher compression. To realize high-quality recovery, the relationship between the compressed image and the discarded data is constructed in a learning manner by means of the designed RC system, which is conducive to the reconstruction process, resulting in lossless recovery. This work can effectively overcome the limitations of existing schemes in terms of simultaneously achieving both well-performing compression and lossless reconstruction. Experimental results are given which demonstrate the high security and superior performance of the proposed scheme.

关键词： Optical dynamics system Reservoir computing Lossless compression Image encryption-then-compression

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A distributed algorithm based on relaxed ADMM for energy resources coordination

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2022年 135卷 107482-107482页

作者： Liu, Hongli Shi, Yawei Wang, Zheng Ran, Liang Lu, Qingguo Li, Huaqing Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

Distributed optimization algorithms contribute to an insight for the optimal coordination of distributed energy resources (DERs). To tackle the DER coordination problem, this paper studies a composite constrained optimization problem over multi-agent networks, where all agents independently maintain convex objective functions while satisfying the local and coupled constraints. Within the Peaceman-Rachford splitting (PRS) framework, a distributed algorithm based on the relaxed ADMM method is proposed for the considered problem, where agents in the network commonly share a fixed step-size instead of the diminishing one. Finally, the simulations on the optimal coordination of DERs including distributed generators (DGs) and energy storages (ESs) are conducted to validate the effectiveness of the proposed algorithm.

关键词： Distributed convex optimization Relaxed ADMM Distributed energy resources Optimization coordination

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Distributed peer-to-peer energy trading framework with manufacturing assembly process and uncertain renewable energy plants in multi-industrial micro-grids

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ENERGY 2024年 302卷

作者： Wu, Qian Song, Qiankun He, Xing Chen, Guo Huang, Tingwen Chongqing Jiaotong Univ Sch Econ & Management Chongqing 400074 Peoples R China Chongqing Jiaotong Univ Sch Math & Stat Chongqing 400074 Peoples R China Southwest Univ Sch Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Texas A&M Univ Qatar Dept Math Doha 23874 Qatar

In this paper, a peer -to -peer energy trading framework for multi -industrial micro -grids is proposed to promote the efficient utilization of local renewable energy resources. There are two levels in the proposed framework. In the upper level, each industrial micro -grid chooses to sell or buy electricity with neighbors by responding to renewable energy generation, where the manufacturing sequences within typical assembly processes are characterized as deferrable loads, thereby offering flexible adjustments for energy trading. Simultaneously, the intricate spatiotemporal coupling between production and assembly lines is thoroughly contemplated and formulated as two distinct sets of constraints. Moreover, uncertainties stemming from renewable energy are articulated through a joint distributionally robust chance constraint, and are subsequently transformed utilizing the Wasserstein metric. In the lower level, the non -cooperative game in market participants is conceptualized to determining the price of energy trading. Following this, a distributed hybrid algorithm combining improved constrained differential evolution and neurodynamic approach is introduced. This innovative blend not only markedly augments the quality of the solutions but also proficiently addresses the continuous distributed peerto -peer energy trading problem. In the end, the case studies illustrate the effectiveness of the proposed hybrid algorithm and highlight the imperative for micro -grids to engage in energy trading.

关键词： P2P energy trading Photovoltaic uncertainty Manufacturing assembly process Neurodynamic algorithm Two-level optimization Demand response

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An Edge-based Distributed Algorithm for Economic Dispatch in Power Systems 16

An Edge-based Distributed Algorithm for Economic Dispatch in...

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16th IEEE International Conference on Control and Automation (ICCA)

作者： Zheng, Lifeng Du, Zhenyuan Li, Huaqing Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China

ISBN: (纸本)9781728190938

This paper proposes an edge-based distributed primal-dual algorithm for economic dispatch problem (EDP), in which a collection of networked generators cooperatively minimizes the sum of all local cost functions while satisfying some coupling constraints. Different from the existing results, this paper applies the techniques, such as the factorization of weighted Laplacian and the spectral decomposition, to prove the convergence of the proposed algorithm. It proves that the proposed distributed algorithm converges provided that the step-size is positive and lower than an exact upper bound. Strong convexity of each local cost function and Lipschitz continuity of its gradient are employed to ensure a linear convergence rate of the proposed algorithm. Finally, two numerical simulations are presented to show the performance of the proposed algorithm.

关键词： Cost functions

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