Showing papers on "Adaptive algorithm published in 2021"
TL;DR: A hardware-efficient variant of ADAPT-VQE that drastically reduces circuit depths using an operator pool that is guaranteed to contain the operators necessary to construct exact ans\"atze and shows that the minimal pool size that achieves this scales linearly with the number of qubits.
Abstract: The resources required to run a high-accuracy variational quantum eigensolver algorithm with a dynamically created ansatz are quantified and reduced significantly, easing the quantum simulation of many-body systems.
211 citations
TL;DR: This paper investigates the resource allocation problem for a group of agents communicating over a strongly connected directed graph, where the total objective function of the problem is composted of the sum of the local objective functions incurred by the agents.
Abstract: This paper investigates the resource allocation problem for a group of agents communicating over a strongly connected directed graph, where the total objective function of the problem is composted of the sum of the local objective functions incurred by the agents. With local convex sets, we first design a continuous-time projection algorithm over a strongly connected and weight-balanced directed graph. Our convergence analysis indicates that when the local objective functions are strongly convex, the output state of the projection algorithm could asymptotically converge to the optimal solution of the resource allocation problem. In particular, when the projection operation is not involved, we show the exponential convergence at the equilibrium point of the algorithm. Second, we propose an adaptive continuous-time gradient algorithm over a strongly connected and weight-unbalanced directed graph for the reduced case without local convex sets. In this case, we prove that the adaptive algorithm converges exponentially to the optimal solution of the considered problem, where the local objective functions and their gradients satisfy strong convexity and Lipachitz conditions, respectively. Numerical simulations illustrate the performance of our algorithms.
70 citations
TL;DR: This article proposes a novel adaptive algorithm for the control gain, which can regulate itself based on the control errors timely and accurately and is model-free, fast response, and accurate.
Abstract: For high control performance of cable-driven manipulators, we design a new adaptive time-delay control (ATDC) using enhanced nonsingular fast terminal sliding mode (NFTSM). The proposed ATDC uses time-delay estimation (TDE) to acquire the lumped dynamics in a simple way and founds a practical model-free structure. Then, a new enhanced NFTSM surface is developed to ensure fast convergence and high control accuracy. To acquire good comprehensive performance under lumped uncertainties, in this article we propose a novel adaptive algorithm for the control gain, which can regulate itself based on the control errors timely and accurately. Benefitting from the TDE and the proposed enhanced NFTSM surface and adaptive control gain, our proposed ATDC is model-free , fast response , and accurate . Theoretical analysis concerning system stability, and control precision and convergence speed are given based on Lyapunov theory. Finally, the advantages of our ATDC over existing methods are verified with comparative experiments.
52 citations
TL;DR: A novel combined fitness function using weight factors and normalization is constructed and solved by a genetic algorithm, and an adaptive algorithm using an iterative process involving weight-factor updating is established.
52 citations
TL;DR: The adaptive algorithm is proved computationally efficient and very accurate for successful FD under large temperature and irradiance variations with noisy measurements for Grid-connected PV systems under Power Point Tracking (PPT) modes during large variations.
48 citations
TL;DR: A new perspective regarding the fractional least mean square (FLMS) adaptive algorithm, called multi innovation FLMS (MIFLMS), is presented, which yields better convergence speed than the standard FLMS by increasing the length of innovation vector.
46 citations
TL;DR: A novel prescribed performance based model-free adaptive sliding mode constrained control strategy is studied which relies on the input/output data of plant rather than the specific model information via a pseudo partial derivative (PPD) parameter to achieve high control accuracy in many actual nonlinear systems with unknown dynamics.
46 citations
TL;DR: To fast approximate maximum likelihood estimators with massive data, this paper studies the Optimal Subsampling Method under the A-optimality Criterion (OSMAC) for generalized linear models, and the consistency and asymptotic normality of the estimator from a general subsampling algorithm are established.
Abstract: To fast approximate maximum likelihood estimators with massive data, this paper studies the Optimal Subsampling Method under the A-optimality Criterion (OSMAC) for generalized linear models The consistency and asymptotic normality of the estimator from a general subsampling algorithm are established, and optimal subsampling probabilities under the A- and L-optimality criteria are derived Furthermore, using Frobenius norm matrix concentration inequalities, finite sample properties of the subsample estimator based on optimal subsampling probabilities are also derived Since the optimal subsampling probabilities depend on the full data estimate, an adaptive two-step algorithm is developed Asymptotic normality and optimality of the estimator from this adaptive algorithm are established The proposed methods are illustrated and evaluated through numerical experiments on simulated and real datasets
45 citations
TL;DR: In this article, a model-parameter-free control strategy for the trajectory tracking problem of the autonomous underwater vehicle exposed to external disturbances and actuator failures is provided, where two control architectures have been constructed such that the system states could be forced to the desired trajectories with acceptable performance.
Abstract: This paper provides a model-parameter-free control strategy for the trajectory tracking problem of the autonomous underwater vehicle exposed to external disturbances and actuator failures. Two control architectures have been constructed such that the system states could be forced to the desired trajectories with acceptable performance. By combining sliding mode control (SMC) technology and adaptive algorithm, the first control architecture is developed for tracking missions under healthy actuators. Taking actuator failures scenario into account, system reliability is improved considerably by the utilization of a passive fault-tolerant technology in the second controller. Benefitting from properties of Euler–Lagrange systems, the nonlinear dynamics of the underwater vehicles could be handled properly such that the proposed controllers could be developed without model parameters. Finally, the validity of the proposed controllers is demonstrated by theoretical analysis and numerical simulations.
41 citations
TL;DR: Compared with other typical recursive methods, the proposed RCLL algorithm can obtain superior steady state behavior and better robustness for combating impulsive noises.
Abstract: We propose a recursive constrained least lncosh (RCLL) adaptive algorithm to combat the impulsive noises. In general, the lncosh function is used to develop a new algorithm within the context of constrained adaptive filtering via solving a linear constrained optimization problem, where the lncosh function is a natural logarithm of hyperbolic cosine function, which can be regarded as a combination of mean-square-error (MSE) and mean-absolute-error (MAE) criteria. Compared with other typical recursive methods, the proposed RCLL algorithm can obtain superior steady state behavior and better robustness for combating impulsive noises. Besides, the mean-square convergence condition and theoretical transient mean-square-deviation of the RCLL algorithm is presented. Simulation results verified the theoretical analysis in non-Gaussian noises and shown the superior performance of the proposed RCLL algorithm.
35 citations
TL;DR: By introducing this adaptive GPR model, the number of required function calls has been largely reduced, and the accuracy for estimation of the intersection points has largely improved, especially for highly nonlinear problems with extremely rare events.
TL;DR: The proposed ABSVR is easy to implement since no embedded optimization algorithm nor iso-probabilistic transformation is required, and the results demonstrate the superior performance of ABSVR for structural reliability analysis in terms of accuracy and efficiency.
TL;DR: In this method, the signals used for fault detection are generated by the controller and are merged with control signals and, therefore, the stability of the system can be rigorously proved and the convergence of the image error is proved.
Abstract: This article proposes a novel method as an improvement of the active fault detection method for eliminating the negative influences generated by accessorial signals used in it. In this method, the signals used for fault detection are generated by the controller and are merged with control signals and, therefore, the stability of the system can be rigorously proved. A specific example of the method is presented to detect the locked-motor faults and finish the visual servoing task of a soft manipulator. In the example, the controller stabilizes the system and generates signals used to detect locked-motor faults. In order to enhance the robustness of the method, an artificial potential field is introduced to impose constraints on the Jacobian matrix of the soft manipulator. Based on it, the adaptive algorithm is designed to guarantee the stability of the system, and the convergence of the image error is proved. Simulations and experiments are conducted to verify the performance of the proposed controller and results demonstrate that on the one hand, the system is stable and the image errors converge to zero when no faults occur; on the other hand, the locked-motor faults can be detected quickly and precisely when it happens.
TL;DR: The novel Lyapunov function is constructed to proof the finite-time convergence of the system and the proposed controller is able to achieve smooth regulation of both active and reactive powers quantities to track the optimal powers in finite time.
TL;DR: The experimental results for nine imbalance datasets show that variable-length brain storm optimization algorithm can find better parameters of CCR-ELM, resulting in the better classification accuracy than other evolutionary optimization algorithms, such as GA, PSO, and VPSO.
Abstract: Class-specific cost regulation extreme learning machine (CCR-ELM) can effectively deal with the class imbalance problems. However, its key parameters, including the number of hidden nodes, the input weights, the biases and the tradeoff factors are normally generated randomly or preset by human. Moreover, the number of input weights and biases depend on the size of hidden layer. Inappropriate quantity of hidden nodes may lead to the useless or redundant neuron nodes, and make the whole structure complex, even cause the worse generalization and unstable classification performances. Based on this, an adaptive CCR-ELM with variable-length brain storm optimization algorithm is proposed for the class imbalance learning. Each individual consists of all above parameters of CCR-ELM and its length varies with the number of hidden nodes. A novel mergence operator is presented to incorporate two parent individuals with different length and generate a new individual. The experimental results for nine imbalance datasets show that variable-length brain storm optimization algorithm can find better parameters of CCR-ELM, resulting in the better classification accuracy than other evolutionary optimization algorithms, such as GA, PSO, and VPSO. In addition, the classification performance of the proposed adaptive algorithm is relatively stable under varied imbalance ratios. Applying the proposed algorithm in the fault diagnosis of conveyor belt also proves that ACCR-ELM with VLen-BSO has the better classification performances.
TL;DR: In this paper, the authors proposed an adaptive extended Kalman filter (AEKF) for the estimation of the state of charge (SOC) of the lithium-ion battery.
Abstract: The state of charge(SOC) of lithium-ion battery is an essential parameter of battery management system. Accurate estimation of SOC is conducive to give full play to the capacity and performance of the battery. For the problems of selection of forgetting factor and poor robustness and susceptibility to the noise of extended Kalman filtering algorithm, this paper proposes a SOC estimation method for the lithium-ion battery based on adaptive extended Kalman filter using improved parameter identification. Firstly, the Thevenin equivalent circuit model is established and the recursive least squares with forgetting factor(FFRLS) method is used to achieve the parameter identification. Secondly, an evaluation factor is defined, and fuzzy control is used to realize the mapping between the evaluation factor and the correction value of forgetting factor, so as to realize the adaptive adjustment of forgetting factor. Finally, the noise adaptive algorithm is introduced into the extended Kalman filtering algorithm(AEKF) to estimate the SOC based on the identification results, which is applied to the parameter identification at the next time and executed circularly, so as to realize the accurate estimation of SOC. The experimental results show that the proposed method has good robustness and estimation accuracy compared with other filtering algorithms under different working conditions, state of health(SOH) and temperature.
TL;DR: A robust adaptive algorithm is derived under the minimum error entropy criterion that can perform robustly under impulsive noise and performs better than the recursive least squares and recursive maximum correntropy algorithm.
TL;DR: A fixed-time output regulation control protocol is constructed to cope with the problem of bipartite output consensus and adaptive fixed- time output consensus of heterogeneous systems which is fully distributed without any global information.
Abstract: This paper researches the output consensus problem of heterogeneous linear multi-agent systems with cooperative and antagonistic interactions. Two fixed-time state compensator control approaches, one static dynamic and the other distributed adaptive dynamic, are considered for heterogeneous systems subject to logarithmic quantization. Then, a fixed-time output regulation control protocol is constructed to cope with the problem of bipartite output consensus and adaptive fixed-time output consensus of heterogeneous systems which is fully distributed without any global information. Besides, the fully distributed adaptive algorithm is employed to calculate the system matrix of leader and it’s also effectively eliminated the harmful chattering. Finally, two simulations are carried out to testify the feasibility of theoretical results.
TL;DR: In this article, the authors proposed a third-order MAF-based quasi-type-l phase-locked loop (TQT1-PLL) with a simplified second-order fast delayed signal cancellation (FDSC) based prefiltering stage.
Abstract: The quasi-type-l phase-locked loop (QT1-PLL) is a grid synchronization technique that has become very popular in recent years thanks to its attractive performance such as easy implementation, fast dynamic response, and good accuracy in steady-state operation. However, it is still vulnerable to operation under harmonically distorted grid voltages with frequency drift. This paper proposes a novel QT1-PLL based synchronization algorithm that makes an appropriate combination of two filters’ types: an in-loop third-order moving average filter (MAF) with a reduced window width, and a simplified second-order fast delayed signal cancellation (FDSC) based prefiltering stage. The proposed PLL is named third-order MAF based QT1-PLL (TQT1-PLL). Though both TQT1-PLL's filters do not need any adaptive algorithm, it is able to reject non-triplen odd-harmonics and the fundamental frequency negative sequence (FFNS) even under grid frequency drift. Its correct operation is confirmed through numerical simulations and real-time implementation on a digital signal processor (DSP). Moreover, the obtained results confirm its ability to reduce the ripple in the estimated frequency and phase under distorted grid voltages and off-nominal frequency operation. Authors show also through an analytical development that the topology of the proposed TQT1-PLL can be extended to enable the rejection of the DC-offset.
TL;DR: In this paper, the problem of fuzzy adaptive control design for a class of stochastic nonstrict feedback nonlinear systems with unknown virtual control coefficients and full state constraints was studied.
TL;DR: The proposed WS-VLPSO is utilized as an effective adaptive algorithm for designing optimal IIR filters based on the inclusion of the order as a discrete variable in the particle vector with a weighted sum fitness function.
TL;DR: In this paper, an adaptive finite element method for second-order elliptic PDEs is proposed, where the arising discrete systems are not solved exactly, and the authors prove that the proposed strategy leads to linear convergence with optimal algebraic rates.
Abstract: We consider adaptive finite element methods for second-order elliptic PDEs, where the arising discrete systems are not solved exactly. For contractive iterative solvers, we formulate an adaptive algorithm which monitors and steers the adaptive mesh-refinement as well as the inexact solution of the arising discrete systems. We prove that the proposed strategy leads to linear convergence with optimal algebraic rates. Unlike prior works, however, we focus on convergence rates with respect to the overall computational costs. In explicit terms, the proposed adaptive strategy thus guarantees quasi-optimal computational time. In particular, our analysis covers linear problems, where the linear systems are solved by an optimally preconditioned CG method as well as nonlinear problems with strongly monotone nonlinearity which are linearized by the so-called Zarantonello iteration.
TL;DR: The steady-state Excess Mean Square Error analysis of the standard MVC algorithm is done using the energy conservation relation and quadratic equation that can provide the exact value of theoretical steady state EMSE is obtained.
Abstract: The Maximum Versoria Criterion (MVC) based adaptive algorithm has recently gained the attention of researchers because of its robustness against impulsive interference and reduced complexity. In this brief, the steady-state Excess Mean Square Error (EMSE) analysis of the standard MVC algorithm is done using the energy conservation relation. For the Gaussian noise case, quadratic equation that can provide the exact value of theoretical steady state EMSE is obtained without any assumptions other than the conventional ones and the Price’s theorem. For the non-Gaussian case, an approximate solution using Taylor’s expansion is derived. Experimental results validate the obtained theoretical findings.
TL;DR: This article shows from simulations carried out in the instrumental configuration of SWIM that the Adaptive algorithm has better accuracy and performance than the classical MLE4 algorithm, and the geophysical parameters obtained with real data from SWIM are analyzed with comparisons to reference data sets.
Abstract: The accuracy of sea surface parameters retrieved from altimeter missions is predominantly governed by the choice of the so-called ``retracking'' algorithm, i.e., the model and inversion method implemented to obtain the surface parameters from the backscattered waveform. For continuity reasons, the choice of space agencies is usually to apply the same retracker from one satellite mission to the other to ensure long-time homogeneous series. In this article, taking the opportunity of a new configuration of the nadir pointing measurements onboard the recently launched China France Oceanography Satellite (CFOSAT) with the Surface Waves Investigation and Monitoring (SWIM) instrument (Hauser et al., 2020), the retracking method was upgraded, by implementing a novel algorithm, called ``Adaptive'' retracker. It combines the improvements brought by Poisson et al., (2018) for the estimation of surface parameters from peaked waveforms over sea ice, improvements in the way the instrumental characteristics are considered in the model (mispointing, point target response) and a more accurate consideration of speckle statistics. In this article, we first show from simulations carried out in the instrumental configuration of SWIM that the Adaptive algorithm has better accuracy and performance than the classical MLE4 algorithm. Then, the geophysical parameters obtained with real data from SWIM are analyzed with comparisons to reference data sets (model and products from altimeters). We show that this new algorithm has several benefits with respect to the classical MLE4 method: no need of lookup tables to correct biases, significant noise reduction on all geophysical variables especially the significant wave height, and performance of inversion over a large set of echo shapes, resulting from standard oceanic scenes as well as highly specular conditions such as over bloom or sea ice.
TL;DR: A new adaptive algorithm for solving 2D interpolation problems of large scattered data sets through the radial basis function partition of unity method is presented, able to efficiently deal with scattered data points with highly varying density in the domain.
Abstract: In this article we present a new adaptive algorithm for solving 2D interpolation problems of large scattered data sets through the radial basis function partition of unity method. Unlike other time-consuming schemes this adaptive method is able to efficiently deal with scattered data points with highly varying density in the domain. This target is obtained by decomposing the underlying domain in subdomains of variable size so as to guarantee a suitable number of points within each of them. The localization of such points is done by means of an efficient search procedure that depends on a partition of the domain in square cells. For each subdomain the adaptive process identifies a predefined neighborhood consisting of one or more levels of neighboring cells, which allows us to quickly find all the subdomain points. The algorithm is further devised for an optimal selection of the local shape parameters associated with radial basis function interpolants via leave-one-out cross validation and maximum likelihood estimation techniques. Numerical experiments show good performance of this adaptive algorithm on some test examples with different data distributions. The efficacy of our interpolation scheme is also pointed out by solving real world applications.
TL;DR: By introducing a signed graph to describe the coopetition interactions among network nodes, the mathematical model of multiple memristor-based neural networks with antagonistic interactions is established and two kinds of the novel node- and edge-based adaptive strategies are proposed, respectively.
Abstract: In this article, by introducing a signed graph to describe the coopetition interactions among network nodes, the mathematical model of multiple memristor-based neural networks (MMNNs) with antagonistic interactions is established. Since the cooperative and competitive interactions coexist, the states of MMNNs cannot reach complete synchronization. Instead, they will reach the bipartite synchronization: all nodes’ states will reach an identical absolute value but opposite sign. To reach bipartite synchronization, two kinds of the novel node- and edge-based adaptive strategies are proposed, respectively. First, based on the global information of the network nodes, a node-based adaptive control strategy is constructed to solve the bipartite synchronization problem of MMNNs. Secondly, a local edge-based adaptive algorithm is proposed, where the weight values of edges between two nodes will change according to the designed adaptive law. Finally, two simulation examples validate the effectiveness of the proposed adaptive controllers and bipartite synchronization criteria.
TL;DR: In this paper, an improved ANN model with an Adaptive Backpropagation Algorithm (ABPA) was developed for best practice in the forecasting long-term load demand of electricity.
Abstract: Artificial Neural Networks (ANNs) have been widely used to determine future demand for power in the short, medium, and long terms. However, research has identified that ANNs could cause inaccurate predictions of load when used for long-term forecasting. This inaccuracy is attributed to insufficient training data and increased accumulated errors, especially in long-term estimations. This study develops an improved ANN model with an Adaptive Backpropagation Algorithm (ABPA) for best practice in the forecasting long-term load demand of electricity. The ABPA includes proposing new forecasting formulations that adjust/adapt forecast values, so it takes into consideration the deviation between trained and future input datasets' different behaviours. The architecture of the Multi-Layer Perceptron (MLP) model, along with its traditional Backpropagation Algorithm (BPA), is used as a baseline for the proposed development. The forecasting formula is further improved by introducing adjustment factors to smooth out behavioural differences between the trained and new/future datasets. A computational study based on actual monthly electricity consumption inputs from 2011 to 2020, provided by the Iraqi Ministry of Electricity, is conducted to verify the proposed adaptive algorithm's performance. Different types of energy consumption and the electricity cut period (unsatisfied demand) factor are also considered in this study as vital factors. The developed ANN model, including its proposed ABPA, is then compared with traditional and popular prediction techniques such as regression and other advanced machine learning approaches, including Recurrent Neural Networks (RNNs), to justify its superiority amongst them. The results reveal that the most accurate long-term forecasts with the minimum Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) values of (1.195.650) and (0.045), respectively, are successfully achieved by applying the proposed ABPA. It can be concluded that the proposed ABPA, including the adjustment factor, enables traditional ANN techniques to be efficiently used for long-term forecasting of electricity load demand.
TL;DR: The stability and iterative convergence of the adaptive iterative learning sliding mode controller with time-varying constraints are proved by composite energy function (CEF).
TL;DR: In this article, a novel approach to improve the performance of FxLMS algorithm in the presence of saturation type non-linearity in the secondary path is proposed by minimizing a cost function consisting of two terms.
Abstract: In active noise control systems, the performance of the most celebrated filtered-x-least mean square (FxLMS) adaptive algorithm is degraded in the presence of non-linearity in the secondary path. In this article, we propose a novel approach to improve the performance of FxLMS algorithm in the presence of saturation type non-linearity in the secondary path. In the proposed method, the weights of the controller are adapted by minimizing a cost function consisting of two terms. The first term is the squared instantaneous residual error while the second term is the weighted square of the difference between input and output of (estimated) saturation non-linearity. Consequently, in case the saturation has occurred, the resultant adaptive algorithm minimizes the residual error while keeping the controller output close to its saturation limit. This avoids unnecessary fluctuation of the (controller) weights due to large variation (or windup) of the controller output after the saturation has occurred. Numerical simulations are provided to verify the superiority of the reported approach as compared to existing methods under different conditions.