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28th European Signal Processing Conference, EUSIPCO 2020

作者： Jo, Byeongho Calamia, Paul Facebook Reality Labs RedmondWA United States

ISBN: (纸本)9789082797053

Blind multichannel identification has played a crucial role as a prerequisite for channel equalization, speech dereverberation, and time-delay estimation for decades. algorithms based on cross-relation (CR) errors have been widely studied, however, these algorithms have been reported to be fundamentally vulnerable to additive noise. Consequently, there have been many studies to improve robustness. Among them, the `p-robust normalized multichannel frequency-domain least-mean-square (`p-RNMCFLMS) algorithm was developed recently to blindly identify a single-input-multiple-output system in a reverberant environment. However, the additional penalty functions of conventional algorithms including `p-RNMCFLMS are based only on the magnitude spectra of the microphone channels. In this work, we propose a new penalty function applied to the phase spectra of the system. Furthermore, we develop an extended `p-RNMCFLMS to improve the steady-state performance by discriminating p values on impulse responses separated into significant and insignificant components. Numerical simulation results show that the proposed algorithm outperforms conventional algorithms even in low-SNR conditions. © 2021 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： adaptive algorithms

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An adaptive Upper Threshold Based Gain Function for the ZA-PNLMS Algorithm

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2020年第10期67卷 2274-2278页

作者： Das, Rajib Lochan Trivedi, Vishwas Dhirubhai Ambani Inst Informat & Commun Technol Gandhinagar 382007 India

The recently proposed Zero-Attracting Proportionate Normalized Least Mean Square (ZA-PNLMS) algorithm improves the performance of the PNLMS algorithm for identifying sparse systems. In particular, it keeps the fast initial convergence rate of the PNLMS algorithm, and improves its transient performance by arresting the fall in the convergence rate at the later stage of the adaptation process, and it also improves the steady-state mean square error (MSE). However, the improvement in the steady-state performance is marginal. In this brief, we propose a novel gain function for the ZA-PNLMS algorithm by introducing an adaptive upper threshold parameter. It has two fold implications. First, the proposed threshold parameter truncates the proportional gains of highly active taps when they approach to their steady-states and helps the lesser active taps to converge faster by providing them more gains, and thereby the overall transient performance is further improved. Secondly, it also improves steady-state MSE significantly by reducing the fluctuation of the active taps at the steady-state. Extensive simulation studies have been carried out to justify the improvement obtained by the proposed algorithm.

关键词： Steady-state Convergence Standards adaptive algorithms Circuits and systems Transient analysis Mean square error methods Sparse adaptive filters PNLMS algorithm mean-square error steady-state performance

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Design of Terminal Sliding Mode adaptive Controller for Multiple Mobile Manipulators with Flexible Joints

Design of Terminal Sliding Mode Adaptive Controller for Mult...

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Chinese Intelligent Systems Conference, CISC 2020

作者： Wang, Baigeng Li, Shurong Automation College Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9789811584572

In this paper, a terminal sliding mode adaptive control algorithm is proposed for the control of multiple mobile manipulators. Firstly, the model of each mobile manipulator is established, and the object model is built at the same time. In addition, the influence of the flexible joint is considered in each mobile manipulator model. Secondly, considering the uncertainty of the model, the adaptive algorithm is used to estimate the model error. Then, the terminal sliding mode control algorithm is used to design the controller, which makes the position tracking error asymptotically converge to 0 in finite time and the force error asymptotically converge to 0 in finite time. Finally, simulation example verifies the feasibility of the strategy. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： adaptive algorithms

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Design and Simulation of adaptive Neuro-Fuzzy Inference System Inverse Controller for a Coupled Tank System 10

Design and Simulation of Adaptive Neuro-Fuzzy Inference Syst...

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10th International Conference on ENERGY and ENVIRONMENT, CIEM 2021

作者： Alshareefi, Hamid Lupu, Ciprian Olteanu, Severus Ismail, Laith Faculty of Automatic Control and Computers University Politehnica of Bucharest Bucharest Romania

ISBN: (纸本)9781665445849

Level control of coupled tank systems is an important issue in the industry's process in the presence of nonlinearity. This paper presented an inverse control method based on adaptive Neuro-Fuzzy Inference System (ANFIS). The ANFIS is utilized to design and implement the nonlinear controller, which has the inverse transfer function of the system. There is no conventional direct feedback in the control loop just that in the enhancement period. The system output is used by an adaptive algorithm to adjust the parameters of the controller. The ANFIS inverse controller has been tested on a simulated coupled tank system and for the sake of results comparison, the output response of the simulated system has been compared using the ANFIS inverse controller and the classical PID controller under the same conditions. The simulation of the coupled tank system and ANFIS controller software has been designed and implemented using the LabVIEW software and MathScript facility. © 2021 IEEE.

关键词： adaptive algorithms

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A New Varying-Gain-Exponent-Based Differentiator/Observer: An Efficient Balance Between Linear and Sliding-Mode algorithms

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2020年第12期65卷 5407-5414页

作者： Ghanes, Malek Barbot, Jean-Pierre Fridman, Leonid Levant, Arie Boisliveau, Robert CNRS LS2N Cent Nantes F-44321 Nantes France ENSEA Quartz F-95014 Cergy Pontoise France CNRS LS2N Nantes France Univ Nacl Autonoma Mexico Mexico City 04510 DF Mexico Tel Aviv Univ IL-6997801 Tel Aviv Israel

It is well known that the supertwisting algorithm is robust to matched perturbation but is sensitive to measurement noise. Contrary to this, the classical linear algorithm is less sensitive to measurement noise but less robust to perturbation. To combine both the good accuracy of the supertwisting algorithm with respect to perturbation and the good performance of the linear algorithm with respect to measurement noise, this article proposes a new differentiator/observer with a varying exponent gain alpha whose variation depends on the magnitude of measurement noise (high-frequency signal). When the magnitude of measurement noise increases (respectively, decreases) alpha tends to 1 (respectively, tends to 0.5) and the proposed differentiator/observer behaves as a linear algorithm (respectively, as a supertwisting algorithm). Thus, by one parameter alpha, the differentiator/observer can take care of high-frequency noise and matched perturbations. A complete stability analysis of the proposed differentiator/observer is provided. To highlight the applicability of the proposed methodology, the dedicated differentiator/observer is, respectively, validated on the electropneumatic actuator and electric machine test benches. These experimental results are compared to those of linear and supertwisting algorithms.

关键词： Perturbation methods Noise measurement Observers Robustness Convergence Gain measurement adaptive algorithms obsevers state estimation

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Aperture-Level Simultaneous Transmit and Receive With Digital Phased Arrays

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2020年第0期68卷 1243-1258页

作者： Cummings, Ian T. Doane, Jonathan P. Schulz, Timothy J. Havens, Timothy C. Michigan Technol Univ Dept Elect & Comp Engn Houghton MI 49931 USA MIT Lincoln Lab Grp 86 RF Technol Lexington MA 02421 USA Michigan Technol Univ Dept Comp Sci Houghton MI 49931 USA

While purely digital phased arrays were once discarded as simultaneous transmit and receive (STAR) capable platforms, this notion has recently been reconsidered. Previous work demonstrated that adaptive digital beamforming and digital self-interference cancellation (SIC) can enable transmitting and receiving subapertures in an array to operate simultaneously in the same frequency band. This approach, referred to as Aperture-Level Simultaneous Transmit and Receive (ALSTAR), uses only adaptive digital beamforming and digital SIC techniques. The ALSTAR architecture does not require custom radiators or analog canceling circuits that can increase front end losses and add significant size, weight, and cost to the array. This paper extends the previously proposed effective isotropic isolation (EII) metric to account for fixed dynamic range transmit and receive channels. An alternating optimization procedure that exploits the interdependence of the transmit and receive beamformers is proposed based on the symmetry of the EII metric, achieving higher EII than in previous work. This optimization procedure balances the goal of null-placement for interference and noise rejection with the goal of maintaining high transmit and receive gain. Simulated results are presented for a-element array that achieves dB of EII in narrowband operation with W of transmit power. We explore the effectiveness of the architecture and proposed optimization methods by demonstrating the high EII achieved across the full scan space of the array at several transmit power levels. Results are also presented for a regularized version of the beamformer optimization problem that allows the designer to trade EII for array gain.

关键词： adaptive arrays phased arrays adaptive algorithms signal processing algorithms iterative algorithms optimization beam steering adaptive filters array signal processing digital signal processing digital filters AWGN signal to noise ratio RF signals

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adaptive procedures for meshfree RBF unsymmetric and symmetric collocation methods

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APPLIED MATHEMATICS AND COMPUTATION 2020年 382卷 125354-125354页

作者： Cavoretto, Roberto De Rossi, Alessandra Univ Torino Dept Math Giuseppe Peano Via Carlo Alberto 10 I-10123 Turin Italy

In this article we present an adaptive scheme for solving radial basis function collocation problems, which involve elliptic partial differential equations. The proposed algorithm is applied to two usual numerical methods, known as the nonsymmetric Kansa's method and the symmetric Hermite-based approach. Basically, the refinement algorithm is firstly characterized by the use of an adaptive superimposition scheme, in which an error estimate compares two approximate solutions computed on a coarser and a finer set of collocation points, and then on a modified adaptive residual subsampling scheme. Blending these computational techniques we detect the areas that need to be refined, also having the chance to further add and/or remove adaptively collocation points. Our study is supported by several numerical results, which illustrate the performance of our iterative algorithm. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Meshfree approximation adaptive algorithms Refinement schemes Collocation methods Partial differential equations

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Quickest Detection of Dynamic Events in Networks

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IEEE TRANSACTIONS ON INFORMATION THEORY 2020年第4期66卷 2280-2295页

作者： Zou, Shaofeng Veeravalli, Venugopal V. Li, Jian Towsley, Don SUNY Buffalo Dept Elect Engn Buffalo NY 14221 USA Univ Illinois ECE Dept Coordinated Sci Lab Urbana IL 61801 USA Univ Illinois Dept Stat Urbana IL 61801 USA SUNY Binghamton Dept Elect & Comp Engn Binghamton NY 13902 USA Univ Massachusetts Amherst Coll Informat & Comp Sci Amherst MA 01003 USA

The problem of quickest detection of dynamic events in networks is studied. At some unknown time, an event occurs, and a number of nodes in the network are affected by the event, in that they undergo a change in the statistics of their observations. It is assumed that the event is dynamic, in that it can propagate along the edges in the network, and affect more and more nodes with time. The event propagation dynamics is assumed to be unknown. The goal is to design a sequential algorithm that can detect a "significant" event, i.e., when the event has affected no fewer than nodes, as quickly as possible, while controlling the false alarm rate. Fully connected networks are studied first, and the results are then extended to arbitrarily connected networks. The designed algorithms are shown to be adaptive to the unknown propagation dynamics, and their first-order asymptotic optimality is demonstrated as the false alarm rate goes to zero. The algorithms can be implemented with linear computational complexity in the network size at each time step, which is critical for online implementation. Numerical simulations are provided to validate the theoretical results.

关键词： Heuristic algorithms Approximation algorithms Transient analysis Computational complexity Power system dynamics Image edge detection Signal processing algorithms adaptive algorithms asymptotic optimality event propagation Network-CuSum (N-CuSum) Spartan-CuSum (S-CuSum) structured network

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Recent Meta-Heuristics Improved by Self-Adaptation Applied to Nonlinear Model-Based Predictive Control

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IEEE ACCESS 2020年 8卷 118841-118852页

作者： Mesquita, Eduardo De Mendonca Sampaio, Renato Coral Hultmann Ayala, Helon Vicente Llanos, Carlos Humberto Univ Fed Goias Sch Elect & Comp Engn BR-74605010 Goiania Go Brazil Univ Brasilia Fac Gama BR-72444240 Brasilia DF Brazil Univ Brasilia Fac Technol BR-70 91090 Brasilia DF Brazil Pontifical Catholic Univ Rio De Janeiro Dept Mech Engn BR-22451900 Rio De Janeiro Brazil

Nonlinear Model-based Predictive Control (NMPC) is a relevant research area having applications in the industrial sector. Traditionally, in this technique, gradient descent algorithms have been used to solve the related optimization problem. More recently, bio-inspired meta-heuristics have also been applied to this problem. However, only a few works have been devoted to testing solvers that use parameter control with self-adaptive traits, which allows mitigating the problem of offline parameter tuning in bio-inspired approaches. In this paper, we propose the novel adaptive Modified Grey Wolf Optimization (AMGWO) and the adaptive Moth-Flame Optimization (AMFO), for solving Nonlinear Model-based Predictive Control (NMPC) problems. To achieve this, a mechanism for individual leaders weighting and a crossover operator are introduced in AMGWO, and a simple self-adaptive parameter technique is applied in both meta-heuristics. The improved solvers are tested to perform the swing-up of a single inverted pendulum and attitude control of a satellite, which are nonlinear problems relevant for assessing control performance. Nonparametric statistical tests are applied to compare the improved meta-heuristics optimization outcomes with other five meta-heuristics, which shows that the self-adaptive parameter technique can significantly improve the performance when applied as an NMPC solver, as the AMFO and AMGWO statistically outperform or performs as well as all algorithms compared in both the pendulum and satellite control, respectively. This is important as improving the optimizer efficiency will lead to more accurate control and enable rapid hardware implementation.

关键词： adaptive algorithms computation intelligence nonlinear dynamical systems predictive control

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A modular analysis of adaptive (non-)convex optimization: Optimism, composite objectives, variance reduction, and variational bounds

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THEORETICAL COMPUTER SCIENCE 2020年 808卷 108-138页

作者： Joulani, Pooria Gyorgy, Andras Szepesvari, Csaba Deepmind London England Univ Alberta Edmonton AB Canada Imperial Coll London London England

Recently, much work has been done on extending the scope of online learning and incremental stochastic optimization algorithms. In this paper we contribute to this effort in two ways: First, based on a generalization of Bregman divergences and a generic regret decomposition, we provide a self-contained, modular analysis of the two workhorses of online learning: (general) adaptive versions of Mirror Descent (MD) and the Follow-the-Regularized-Leader (FTRL) algorithms. The analysis is done with extra care so as not to introduce assumptions not needed in the proofs and allows to combine, in a straightforward way, different algorithmic ideas (e.g., adaptivity, optimism, implicit updates, variance reduction) and learning settings (e.g., strongly convex or composite objectives). This way we are able to reprove, extend and refine a large body of the literature, while keeping the proofs concise. The second contribution is a by-product of this careful analysis: We present algorithms with improved variational bounds for smooth, composite objectives, including a new family of optimistic MD algorithms with only one projection step per round. Furthermore, we provide a simple extension of adaptive regret bounds to a class of practically relevant non-convex problem settings (namely, star-convex loss functions and their extensions) with essentially no extra effort. (C) 2019 Published by Elsevier B.V.

关键词： Online learning Stochastic optimization adaptive algorithms Regret bound Follow the regularized leader Mirror descent

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