Showing papers on "Adaptive algorithm published in 2015"
TL;DR: In this article, the adaptive parameter estimation and control for nonlinear robotic systems based on parameter estimation errors is studied, where three adaptive laws driven by the estimation error are presented, where exponential error convergence is proved under the conventional persistent excitation (PE) condition; the direct measurement of the time derivatives of the system states are avoided.
Abstract: Summary
This paper studies adaptive parameter estimation and control for nonlinear robotic systems based on parameter estimation errors. A framework to obtain an expression of the parameter estimation error is proposed first by introducing a set of auxiliary filtered variables. Then three novel adaptive laws driven by the estimation error are presented, where exponential error convergence is proved under the conventional persistent excitation (PE) condition; the direct measurement of the time derivatives of the system states are avoided. The adaptive laws are modified via a sliding mode technique to achieve finite-time convergence, and an online verification of the alternative PE condition is introduced. Leakage terms, functions of the estimation error, are incorporated into the adaptation laws to avoid windup of the adaptation algorithms. The adaptive algorithm applied to robotic systems permits that tracking control and exact parameter estimation are achieved simultaneously in finite time using a terminal sliding mode (TSM) control law. In this case, the PE condition can be replaced with a sufficient richness requirement of the command signals and thus is verifiable a priori. The potential singularity problem encountered in TSM controls is remedied by introducing a two-phase control procedure. The robustness of the proposed methods against disturbances is investigated. Simulations based on the ‘Bristol-Elumotion-Robotic-Torso II’ (BERT II) are provided to validate the efficacy of the introduced methods. Copyright © 2014 John Wiley & Sons, Ltd.
270 citations
TL;DR: In this paper, pinning synchronization problem for nonlinear coupled networks is investigated, which can be recurrently connected neural networks, cellular Neural networks, Hodgkin-Huxley models, Lorenz chaotic oscillators, and so on.
Abstract: In this paper, pinning synchronization problem for nonlinear coupled networks is investigated, which can be recurrently connected neural networks, cellular neural networks, Hodgkin–Huxley models, Lorenz chaotic oscillators, and so on. Nodes in the network are assumed to be identical and nodes’ dynamical behaviors are described by continuous-time equations. The network topology is undirected and static. At first, the scope of accepted nonlinear coupling functions is defined, and the effect of nonlinear coupling functions on synchronization is carefully discussed. Then, the pinning control technique is used for synchronization, especially the control type is aperiodically intermittent. Some sufficient conditions to guarantee global synchronization are presented. Furthermore, the adaptive approach is also applied on the pinning control, and a centralized adaptive algorithm is designed and its validity is also proved. Finally, several numerical simulations are given to verify the obtained theoretical results.
225 citations
TL;DR: PC-Kriging is derived as a new non-intrusive meta-modeling approach combining PCE and Kriging, which approximates the global behavior of the computational model whereas Kriged manages the local variability of the model output.
Abstract: Computer simulation has become the standard tool in many engineering fields for designing and optimizing systems, as well as for assessing their reliability. Optimization and uncertainty quantification problems typically require a large number of runs of the computational model at hand, which may not be feasible with high-fidelity models directly. Thus surrogate models (a.k.a metamodels) have been increasingly investigated in the last decade. Polynomial Chaos Expansions (PCE) and Kriging are two popular non-intrusive metamodelling techniques. PCE surrogates the computational model with a series of orthonormal polynomials in the input variables where polynomials are chosen in coherency with the probability distributions of those input variables. A least-square minimization technique may be used to determine the coefficients of the PCE. On the other hand, Kriging assumes that the computer model behaves as a realization of a Gaussian random process whose parameters are estimated from the available computer runs, i.e. input vectors and response values. These two techniques have been developed more or less in parallel so far with little interaction between the researchers in the two fields. In this paper, PC-Kriging is derived as a new non-intrusive meta-modeling approach combining PCE and Kriging. A sparse set of orthonormal polynomials (PCE) approximates the global behavior of the computational model whereas Kriging manages the local variability of the model output. An adaptive algorithm similar to the least angle regression algorithm determines the optimal sparse set of polynomials. PC-Kriging is validated on various benchmark analytical functions which are easy to sample for reference results. From the numerical investigations it is concluded that PC-Kriging performs better than or at least as good as the two distinct meta-modeling techniques. A larger gain in accuracy is obtained when the experimental design has a limited size, which is an asset when dealing with demanding computational models.
220 citations
TL;DR: A robust kernel adaptive algorithm is derived in kernel space and under the maximum correntropy criterion (MCC), which is particularly useful for nonlinear and non-Gaussian signal processing, especially when data contain large outliers or disturbed by impulsive noises.
136 citations
TL;DR: This paper experimentally demonstrates a high-speed RGB-LED based WDM VLC system employing carrier-less amplitude and phase (CAP) modulation and recursive least square (RLS) based adaptive equalization, and achieves the highest data rate ever achieved in RGB- LED based VLC systems.
Abstract: Inter-symbol interference (ISI) is one of the key problems that seriously limit transmission data rate in high-speed VLC systems. To eliminate ISI and further improve the system performance, series of equalization schemes have been widely investigated. As an adaptive algorithm commonly used in wireless communication, RLS is also suitable for visible light communication due to its quick convergence and better performance. In this paper, for the first time we experimentally demonstrate a high-speed RGB-LED based WDM VLC system employing carrier-less amplitude and phase (CAP) modulation and recursive least square (RLS) based adaptive equalization. An aggregate data rate of 4.5Gb/s is successfully achieved over 1.5-m indoor free space transmission with the bit error rate (BER) below the 7% forward error correction (FEC) limit of 3.8x10(-3). To the best of our knowledge, this is the highest data rate ever achieved in RGB-LED based VLC systems.
132 citations
TL;DR: The adaptive control design to solve the trajectory tracking problem of a Delta robot with uncertain dynamical model showed a better performance than the regular proportional-integral-derivative (PID) controller with feed-forward actions as well as a nonadaptive active disturbance rejection controller.
Abstract: This paper describes the adaptive control design to solve the trajectory tracking problem of a Delta robot with uncertain dynamical model. This robot is a fully actuated, parallel closed-chain device. The output-based adaptive control was designed within the active disturbance rejection framework. An adaptive nonparametric representation for the uncertain section of the robot model was obtained using an adaptive least mean squares procedure. The adaptive algorithm was designed without considering the velocity measurements of the robot joints. Therefore, a simultaneous observer–identifier scheme was the core of the control design. A set of experimental tests were developed to prove the performance of the algorithm presented in this paper. Some reference trajectories were proposed which were successfully tracked by the robot. In all the experiments, the adaptive scheme showed a better performance than the regular proportional-integral-derivative (PID) controller with feed-forward actions as well as a nonadaptive active disturbance rejection controller. A set of numerical simulations was developed to show that even under five times faster reference trajectories, the adaptive controller showed better results than the PID controller.
125 citations
TL;DR: In this article, the authors proposed 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.
Abstract: 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].
78 citations
TL;DR: An adaptive algorithm for determining the triggering threshold is developed, which allows the intelligent sensors to tune the boundary of a local event domain in an online manner, so as to keep the average transmission rate level off a desired value.
Abstract: In this paper, we investigate the distributed filtering problem over wireless sensor networks (WSNs) with bandwidth and energy constraints. To utilize the limited resources efficiently, a novel event-based mechanism is proposed for the sensor node, such that only selected valuable data are broadcasted to its neighboring sensors via the wireless channel according to whether specific events happen. By resorting to graph theory and utilizing stochastic analysis methods, the filter parameters and the event triggering rules are designed, such that the filtering error converges at an exponential rate in the mean square sense. An adaptive algorithm for determining the triggering threshold is developed, which allows the intelligent sensors to tune the boundary of a local event domain in an online manner, so as to keep the average transmission rate level off a desired value. An illustrative example is given to demonstrate the effectiveness of the proposed strategy.
76 citations
TL;DR: A new adaptive algorithm called block-sparse LMS (BS-LMS) is proposed, to insert a penalty of block-sparsity, which is a mixed l2, 0 norm of adaptive tap-weights with equal group partition sizes, into the cost function of traditional LMS algorithm.
Abstract: In order to improve the performance of least mean square (LMS)-based adaptive filtering for identifying block-sparse systems, a new adaptive algorithm called block-sparse LMS (BS-LMS) is proposed in this paper. The basis of the proposed algorithm is to insert a penalty of block-sparsity, which is a mixed l2, 0 norm of adaptive tap-weights with equal group partition sizes, into the cost function of traditional LMS algorithm. To describe a block-sparse system response, we first propose a Markov-Gaussian model, which can generate a kind of system responses of arbitrary average sparsity and arbitrary average block length using given parameters. Then we present theoretical expressions of the steady-state misadjustment and transient convergence behavior of BS-LMS with an appropriate group partition size for white Gaussian input data. Based on the above results, we theoretically demonstrate that BS-LMS has much better convergence behavior than l0-LMS with the same small level of misadjustment. Finally, numerical experiments verify that all of the theoretical analysis agrees well with simulation results in a large range of parameters.
72 citations
TL;DR: A distributed adaptive algorithm to solve a node-specific parameter estimation problem where the nodes are interested in estimating parameters that can be of local interest, common interest to a subset of nodes and global interest to the whole network is proposed.
Abstract: A distributed adaptive algorithm is proposed to solve a node-specific parameter estimation problem where the nodes are interested in estimating parameters that can be of local interest, common interest to a subset of nodes and global interest to the whole network. To address the different node-specific parameter estimation problems, this novel algorithm relies on a diffusion-based implementation of different, yet coupled Least Mean Squares (LMS) algorithms, each associated with the estimation of a specific set of local, common or global parameters. The study of convergence in the mean sense reveals that the proposed algorithm is asymptotically unbiased. Moreover, a spatial-temporal energy conservation relation is provided to evaluate the steady-state performance at each node in the mean-square sense. Finally, the theoretical results and the effectiveness of the proposed technique are validated through computer simulations in the context of cooperative spectrum sensing in Cognitive Radio networks.
71 citations
TL;DR: By applying the adaptive approach, a centralized adaptive algorithm on the intermittent control gain is designed, and its validity rigorously is proved.
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TL;DR: PC-Kriging as discussed by the authors is a meta-modeling approach combining Polynomial Chaos Expansions (PCE) and Kriging, where PCE surrogates the computational model with a series of orthonormal polynomials in the input variables where polynomial are chosen in coherency with the probability distributions of those input variables.
Abstract: Computer simulation has become the standard tool in many engineering fields for designing and optimizing systems, as well as for assessing their reliability. To cope with demanding analysis such as optimization and reliability, surrogate models (a.k.a meta-models) have been increasingly investigated in the last decade. Polynomial Chaos Expansions (PCE) and Kriging are two popular non-intrusive meta-modelling techniques. PCE surrogates the computational model with a series of orthonormal polynomials in the input variables where polynomials are chosen in coherency with the probability distributions of those input variables. On the other hand, Kriging assumes that the computer model behaves as a realization of a Gaussian random process whose parameters are estimated from the available computer runs, i.e. input vectors and response values. These two techniques have been developed more or less in parallel so far with little interaction between the researchers in the two fields. In this paper, PC-Kriging is derived as a new non-intrusive meta-modeling approach combining PCE and Kriging. A sparse set of orthonormal polynomials (PCE) approximates the global behavior of the computational model whereas Kriging manages the local variability of the model output. An adaptive algorithm similar to the least angle regression algorithm determines the optimal sparse set of polynomials. PC-Kriging is validated on various benchmark analytical functions which are easy to sample for reference results. From the numerical investigations it is concluded that PC-Kriging performs better than or at least as good as the two distinct meta-modeling techniques. A larger gain in accuracy is obtained when the experimental design has a limited size, which is an asset when dealing with demanding computational models.
TL;DR: It is shown that the sequences of triangulations which are produced by the algorithm in the FE discretization of the active gpc coefficients are asymptotically optimal.
Abstract: We analyze a posteriori error estimation and adaptive refinement algorithms for stochastic Galerkin Finite Element methods for countably-parametric, elliptic boundary value problems. A residual error estimator which separates the effects of gpc-Galerkin discretization in parameter space and of the Finite Element discretization in physical space in energy norm is established. It is proved that the adaptive algorithm converges. To this end, a contraction property of its iterates is proved. It is shown that the sequences of triangulations which are produced by the algorithm in the FE discretization of the active gpc coefficients are asymptotically optimal. Numerical experiments illustrate the theoretical results.
TL;DR: A dynamic energy-efficient algorithm, which computes the amount of power allocation in each access point based on the buffer backlog size and channel states under the consideration of buffer stability is proposed.
Abstract: This paper proposes a polynomial-time algorithm for energy-efficient dynamic packet downloading from medical cloud storage to medical Internet-of-Things (IoT) devices. The medical cloud can distribute its own medical data to medical IoT devices via access points. Therefore, network disconnection can happen between the medical cloud and medical IoT devices when power/energy management in each access point is not efficient. This situation is especially harmful in in-hospital network architectures, because the architecture usually has strict requirements in terms of reliability. Therefore, this paper proposes a dynamic energy-efficient algorithm, which computes the amount of power allocation in each access point based on the buffer backlog size and channel states under the consideration of buffer stability. With the proposed adaptive algorithm, each access point calibrates its own parameters for more adaptive power/energy management. The performance of the proposed algorithm is evaluated in terms of network lifetime, and it is observed that the proposed algorithm achieves the desired performance.
TL;DR: In the presence of frequency selective crosstalk, an extension of CTC-DPD which can pre-cancel such cros stalk, performs considerably better than CO-D PD, which cannot model the frequency selectivity of the crosStalk.
Abstract: This paper presents a comparative study of adaptive algorithms for digital predistortion (DPD) in multiple antenna transmitters. Crossover predistorter (CO-DPD) and crosstalk canceling predistorter (CTC-DPD) were proposed to overcome the deleterious effect of RF crosstalk before the power amplifiers (PA) on digital predistortion (DPD) in multiple antenna transmitters. This paper discusses the linearization performance and computational complexity of least mean square (LMS) and recursive least squares (RLS) adaptive algorithms for CO-DPD and CTC-DPD. The adaptive predistortion algorithms for a single antenna transmitter can be extended for CO-DPD, by incorporating the DPD coefficients of more than one branch in to one filter coefficient vector of the adaptive algorithm. The adaptive CTC-DPD involves predistorters for each transmitter running in parallel with the adaptive algorithms that track the coupling between the transmitters. The computational complexity of CTC-DPD is considerably lower compared to CO-DPD, as it has lesser predistorter branches. It is estimated that the number of computations needed per sample duration, for the real-time operation of the adaptive CTC-DPD is 47% lesser compared to CO-DPD for a two-antenna transmitter, when a 9th order memory polynomial with 3 memory taps was used. The linearization performances of these adaptive predistortion techniques for two and four antenna transmitters are evaluated through simulations using QPSK, 16-QAM, UMTS, and LTE signals. It is observed that CTC-DPD performs approximately identical to CO-DPD in all the examined cases. In the presence of frequency selective crosstalk, an extension of CTC-DPD which can pre-cancel such crosstalk, performs considerably better than CO-DPD, which cannot model the frequency selectivity of the crosstalk.
TL;DR: A new CA local rule with adaptive neighborhood type to produce the edge map of image as opposed to CA with fixed neighborhood type, which uses the von Neumann and Moore neighborhood types.
Abstract: In this paper, we propose a cellular edge detection (CED) algorithm which utilizes cellular automata (CA) and cellular learning automata (CLA). The CED algorithm is an adaptive, intelligent and learnable algorithm for edge detection of binary and grayscale images. Here, we introduce a new CA local rule with adaptive neighborhood type to produce the edge map of image as opposed to CA with fixed neighborhood. The proposed adaptive algorithm uses the von Neumann and Moore neighborhood types. Experimental results demonstrate that the CED algorithm has superior accuracy and performance in contrast to other edge detection methods such as Sobel, Prewitt, Robert, LoG and Canny operators. Moreover, the CED algorithm loses fewer details while extracting image edges compare to other edge detection methods.
TL;DR: In this article, a new adaptive signal processing technique for feature extraction and parameter estimation in noisy exponentially damped signals is introduced, based on the adroit integration of the strengths of parametric and nonparametric methods such as multiple signal categorization, matrix pencil, and empirical mode decomposition algorithms.
Abstract: In this paper authors introduce a new adaptive signal processing technique for feature extraction and parameter estimation in noisy exponentially damped signals. The iterative 3-stage method is based on the adroit integration of the strengths of parametric and nonparametric methods such as multiple signal categorization, matrix pencil, and empirical mode decomposition algorithms. The first stage is a new adaptive filtration or noise removal scheme. The second stage is a hybrid parametric–nonparametric signal parameter estimation technique based on an output-only system identification technique. The third stage is optimization of estimated parameters using a combination of the primal-dual path-following interior point algorithm and genetic algorithm. The methodology is evaluated using a synthetic signal and a signal obtained experimentally from transverse vibrations of a steel cantilever beam. The method is successful in estimating the frequencies accurately. Further, it estimates the damping exponents. The proposed adaptive filtration method does not include any frequency domain manipulation. Consequently, the time domain signal is not affected as a result of frequency domain and inverse transformations.
TL;DR: In this article, the authors proposed a robust adaptive algorithm for frequency estimation in three-phase power systems when the voltage readings are corrupted by random noise sources, which employs the Clarke's transformed threephase voltage (a complex signal) and augmented complex statistics to deal with both of balanced and unbalanced system conditions.
Abstract: In this study, the authors propose a robust adaptive algorithm for frequency estimation in three-phase power systems when the voltage readings are corrupted by random noise sources. The proposed algorithm employs the Clarke's transformed three-phase voltage (a complex signal) and augmented complex statistics to deal with both of balanced and unbalanced system conditions. To derive the algorithm, a widely linear predictive model is assumed for the Clarke's transformed signal where the frequency of system is related to the parameters of this model. To estimate the model parameters with noisy voltage reading, they utilise the notions of maximum correntropy criterion and gradient-ascent optimisation. The proposed algorithm has the computational complexity of the popular complex least-mean-squares (CLMS) algorithm, along with the robustness that is obtained by using higher-order moments beyond just second-order moments. They compare the performance of the proposed algorithm with a recently introduced augmented CLMS (ACLMS) algorithm in different conditions, including the voltage sags and presence of impulsive noises and and higher-order harmonics. Their simulation results demonstrate that the proposed algorithm provides improved frequency estimation performance compared with ACLMS especially when the measured voltages are corrupted by impulsive noise.
15 Jun 2015
TL;DR: In this paper, an adaptive algorithm for the 2-set packing problem was proposed, which achieves a (1-e) fraction of the omniscient optimal solution for an arbitrarily small e > 0.
Abstract: The stochastic matching problem deals with finding a maximum matching in a graph whose edges are unknown but can be accessed via queries. This is a special case of stochastic k-set packing, where the problem is to find a maximum packing of sets, each of which exists with some probability. In this paper, we provide edge and set query algorithms for these two problems, respectively, that provably achieve some fraction of the omniscient optimal solution. Our main theoretical result for the stochastic matching (i.e., 2-set packing) problem is the design of an adaptive algorithm that queries only a constant number of edges per vertex and achieves a (1-e) fraction of the omniscient optimal solution, for an arbitrarily small e > 0. Moreover, this adaptive algorithm performs the queries in only a constant number of rounds. We complement this result with a non-adaptive (i.e., one round of queries) algorithm that achieves a (0.5 - e) fraction of the omniscient optimum. We also extend both our results to stochastic k-set packing by designing an adaptive algorithm that achieves a (2/k - e) fraction of the omniscient optimal solution, again with only O(1) queries per element. This guarantee is close to the best known polynomial-time approximation ratio of 3/k+1 -e for the deterministic k-set packing problem [Furer 2013]. We empirically explore the application of (adaptations of) these algorithms to the kidney exchange problem, where patients with end-stage renal failure swap willing but incompatible donors. We show on both generated data and on real data from the first 169 match runs of the UNOS nationwide kidney exchange that even a very small number of non-adaptive edge queries per vertex results in large gains in expected successful matches.
TL;DR: This work considers the Galerkin boundary element method (BEM) for weakly-singular integral equations of the first-kind in 2D and formulates an adaptive algorithm which steers the local mesh-refinement and the multiplicity of the knots.
TL;DR: A novel integrated adaptive algorithm based on independent component analysis (ICA), ensemble empirical mode decomposition (EEMD), and wavelet shrinkage (WS) denoising, denoted as ICA-E EMD-WS, for FECG separation and noise reduction is proposed.
Abstract: High-resolution fetal electrocardiogram (FECG) plays an important role in assisting physicians to detect fetal changes in the womb and to make clinical decisions. However, in real situations, clear FECG is difficult to extract because it is usually overwhelmed by the dominant maternal ECG and other contaminated noise such as baseline wander, high-frequency noise. In this paper, we proposed a novel integrated adaptive algorithm based on independent component analysis (ICA), ensemble empirical mode decomposition (EEMD), and wavelet shrinkage (WS) denoising, denoted as ICA-EEMD-WS, for FECG separation and noise reduction. First, ICA algorithm was used to separate the mixed abdominal ECG signal and to obtain the noisy FECG. Second, the noise in FECG was reduced by a three-step integrated algorithm comprised of EEMD, useful subcomponents statistical inference and WS processing, and partial reconstruction for baseline wander reduction. Finally, we evaluate the proposed algorithm using simulated data sets. The results indicated that the proposed ICA-EEMD-WS outperformed the conventional algorithms in signal denoising.
01 Nov 2015
TL;DR: This paper presents a collaborative and adaptive algorithm for resource-constrained MAV nodes to quickly and efficiently navigate to preassigned locations using radio fingerprints between flying and landed MAVs acting as radio beacons, and detects intersections in trajectories of mobile nodes.
Abstract: Micro-aerial vehicle (MAV) swarms are a new class of mobile sensor networks with many applications, including search and rescue, urban surveillance, radiation monitoring, etc. These sensing applications require autonomously navigating a high number of low-cost, low-complexity MAV sensor nodes in hazardous environments. The lack of preexisting localization infrastructure and the limited sensing, computing, and communication abilities of individual nodes makes it challenging for nodes to autonomously navigate to suitable preassigned locations. In this paper, we present a collaborative and adaptive algorithm for resource-constrained MAV nodes to quickly and efficiently navigate to preassigned locations. Using radio fingerprints between flying and landed MAVs acting as radio beacons, the algorithm detects intersections in trajectories of mobile nodes. The algorithm combines noisy dead-reckoning measurements from multiple MAVs at detected intersections to improve the accuracy of the MAVs' location estimations. In addition, the algorithm plans intersecting trajectories of MAV nodes to aid the location estimation and provide desired performance in terms of timeliness and accuracy of navigation. We evaluate the performance of our algorithm through a real testbed implementation and large-scale physical feature based simulations. Our results show that, compared to existing autonomous navigation strategies, our algorithm achieves up to 6X reduction in location estimation errors, and as much as 3X improvement in navigation success rate under the given time and accuracy constraints.
01 Oct 2015
TL;DR: An advanced discussion mechanism-based brain storm optimization (ADMBSO) algorithm is proposed, pushing forward the study in the incorporation of inter- and intra-cluster discussions into the brainStorm optimization algorithm (BSO), to control global and local searching ability, respectively.
Abstract: Evolutionary computation-based algorithms are successfully developed to handle challenges in optimization problems by applying the analogy to biological systems. We aim at designing advanced optimization algorithms, with inspiration from human's creative problem-solving strategies. In this paper, we proposed an advanced discussion mechanism-based brain storm optimization (ADMBSO) algorithm, pushing forward our study in the incorporation of inter- and intra-cluster discussions into the brain storm optimization algorithm (BSO) to control global and local searching ability, respectively. In the advanced discussion mechanism, elaborately designed inter- and intra-cluster discussions were alternatively performed throughout the optimization process, with the ratio controlled by a linearly adjusted probability. We further introduced a differential step strategy into the workflow, making ADMBSO a more efficient and more adaptive algorithm. Empirical studies on different function optimization problems illustrated the effectiveness and efficiency of the ADMBSO algorithm. Comparisons among the ADMBSO, BSO algorithm, closed-loop brain storm optimization algorithm, particle swarm optimization algorithm, and differential evolution algorithm, have also been provided in detail. As one of the first algorithms inspired by human behavior, ADMBSO demonstrates its great potential in dealing with complex optimization problems.
TL;DR: An online strategy proven to locate the target up to a desired uncertainty level at near-optimal cost is presented and validated in simulations and multiple field experiments performed using autonomous surface vehicles carrying radio antennas to localize radio tags.
Abstract: We study the problem of actively locating a static target using mobile robots equipped with bearing sensors. The goal is to reduce the uncertainty in the target's location to a value below a given threshold in minimum time. Our cost formulation explicitly models time spent in traveling, as well as taking measurements. In addition, we consider distance-based communication constraints between the robots. We provide the following theoretical results. First, we study the properties of an optimal offline strategy for one or more robots with access to the target's true location. We derive the optimal offline algorithm and bound its cost when considering a single robot or an even number of robots. In other cases, we provide a close approximation. Second, we provide a general method of converting the offline algorithm into an online adaptive algorithm (that does not have access to the target's true location), while preserving near optimality. Using these two results, we present an online strategy proven to locate the target up to a desired uncertainty level at near-optimal cost. In addition to theoretical analysis, we validate the algorithm in simulations and multiple field experiments performed using autonomous surface vehicles carrying radio antennas to localize radio tags.
TL;DR: In this paper, a sliding mode observer (SMO) is designed to estimate the state variable of the friction model, and a model reference adaptive control system is proposed to track the desired speed trajectory while alleviating the adverse effects of model uncertainties and friction.
TL;DR: An adaptive algorithm for monitoring big data applications that adapts the intervals of sampling and frequency of updates to data characteristics and administrator needs is presented that reduces monitoring costs significantly without penalizing data quality.
TL;DR: This paper presents a procedure based on the Backward Differentiation Formulas of order 1 to 5 to obtain efficient time integration of the incompressible Navier-Stokes equations and performs both stepsize and order selections to control respectively the solution accuracy and the computational efficiency of the time integration process.
TL;DR: In this article, a global adaptive direct slicing technique of Non-Uniform Rational B-Spline (NURBS)-based sculptured surface for rapid prototyping where the NURBS representation is directly extracted from the computer-aided design (CAD) model.
Abstract: Purpose – This paper aims to propose a global adaptive direct slicing technique of Non-Uniform Rational B-Spline (NURBS)-based sculptured surface for rapid prototyping where the NURBS representation is directly extracted from the computer-aided design (CAD) model. The imported NURBS surface is directly sliced to avoid inaccuracies due to tessellation methods used in common practice. The major objective is to globally optimize texture error function based on the available range of layer thicknesses of the utilized rapid prototyping machine. The total texture error is computed with the defined error function to verify slicing efficiency of this global adaptive slicing algorithm and to find the optimum number of slices. A variety of experiments are conducted to study the accuracy of the developed procedure, and the results are compared with previously developed algorithms. Design/methodology/approach – This paper proposes a new adaptive algorithm which globally optimizes a texture error function produced by ...
TL;DR: A novel adaptive algorithm, which is similar to the Slotine-Li algorithm in model-based adaptive control, to estimate the robot's position by using the tracked feature points in image sequence, the robots' velocity, and orientation angles measured by odometry and inertial sensors is proposed.
Abstract: This paper presents a novel and simple adaptive algorithm for estimating the position of a mobile robot with high accuracy in an unknown and unstructured environment by fusing images of an omnidirectional vision system with measurements of odometry and inertial sensors. Based on a new derivation where the omnidirectional projection can be linearly parameterized by the positions of the robot and natural feature points, we propose a novel adaptive algorithm, which is similar to the Slotine–Li algorithm in model-based adaptive control, to estimate the robot’s position by using the tracked feature points in image sequence, the robot’s velocity, and orientation angles measured by odometry and inertial sensors. It is proved that the adaptive algorithm leads to global exponential convergence of the position estimation errors to zero. Simulations and real-world experiments are performed to demonstrate the performance of the proposed algorithm.
28 Sep 2015
TL;DR: This work combines two highly efficient model reduction strategies, namely a modern low-rank tensor representation in the tensor train format of the problem and a refinement algorithm on the basis of a posteriori error estimates to adaptively adjust the different employed discretizations.
Abstract: The solution of PDE with stochastic data commonly leads to very high-dimensional algebraic problems, e.g. when multiplicative noise is present. The Stochastic Galerkin FEM considered in this paper then suffers from the curse of dimensionality. This is directly related to the number of random variables required for an adequate representation of the random fields included in the PDE. With the presented new approach, we circumvent this major complexity obstacle by combining two highly efficient model reduction strategies, namely a modern low-rank tensor representation in the tensor train format of the problem and a refinement algorithm on the basis of a posteriori error estimates to adaptively adjust the different employed discretizations. The adaptive adjustment includes the refinement of the FE mesh based on a residual estimator, the problem-adapted stochastic discretization in anisotropic Legendre Wiener chaos and the successive increase of the tensor rank. Computable a posteriori error estimators are derived for all error terms emanating from the discretizations and the iterative solution with a preconditioned ALS scheme of the problem. Strikingly, it is possible to exploit the tensor structure of the problem to evaluate all error terms very efficiently. A set of benchmark problems illustrates the performance of the adaptive algorithm with higher-order FE. Moreover, the influence of the tensor rank on the approximation quality is investigated.