Showing papers on "Adaptive algorithm published in 2006"

Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems

Abstract: We describe an efficient technique for adapting control parameter settings associated with differential evolution (DE). The DE algorithm has been used in many practical cases and has demonstrated good convergence properties. It has only a few control parameters, which are kept fixed throughout the entire evolutionary process. However, it is not an easy task to properly set control parameters in DE. We present an algorithm-a new version of the DE algorithm-for obtaining self-adaptive control parameter settings that show good performance on numerical benchmark problems. The results show that our algorithm with self-adaptive control parameter settings is better than, or at least comparable to, the standard DE algorithm and evolutionary algorithms from literature when considering the quality of the solutions obtained

Low Complexity OFDM Detector for Underwater Acoustic Channels

Abstract: An adaptive algorithm is proposed for OFDM signal detection on Doppler-distorted, time-varying multipath channels. The focus of the approach is on low complexity post-FFT signal processing. The receiver performs MMSE combining of signals received across an array, using adaptive channel estimation. Non-uniform Doppler compensation across subbands is performed using a single adaptively estimated parameter representing the Doppler rate. Algorithm performance is demonstrated on experimental data, transmitted through a shallow water channel over the distance of 2.5 km. QPSK modulation with a varying number of carriers is used in a 24 kHz acoustic bandwidth. Excellent performance is achieved with up to 1024 carriers, yielding an overall bit rate of 30 kbps.

Uncalibrated visual servoing of robots using a depth-independent interaction matrix

Abstract: This paper presents a new adaptive controller for image-based dynamic control of a robot manipulator using a fixed camera whose intrinsic and extrinsic parameters are not known. To map the visual signals onto the joints of the robot manipulator, this paper proposes a depth-independent interaction matrix, which differs from the traditional interaction matrix in that it does not depend on the depths of the feature points. Using the depth-independent interaction matrix makes the unknown camera parameters appear linearly in the closed-loop dynamics so that a new algorithm is developed to estimate their values on-line. This adaptive algorithm combines the Slotine-Li method with on-line minimization of the errors between the real and estimated projections of the feature points on the image plane. Based on the nonlinear robot dynamics, we prove asymptotic convergence of the image errors to zero by the Lyapunov theory. Experiments have been conducted to verify the performance of the proposed controller. The results demonstrated good convergence of the image errors

Nonlinear approximation based image recovery using adaptive sparse reconstructions and iterated denoising-part I: theory

Abstract: We study the robust estimation of missing regions in images and video using adaptive, sparse reconstructions. Our primary application is on missing regions of pixels containing textures, edges, and other image features that are not readily handled by prevalent estimation and recovery algorithms. We assume that we are given a linear transform that is expected to provide sparse decompositions over missing regions such that a portion of the transform coefficients over missing regions are zero or close to zero. We adaptively determine these small magnitude coefficients through thresholding, establish sparsity constraints, and estimate missing regions in images using information surrounding these regions. Unlike prevalent algorithms, our approach does not necessitate any complex preconditioning, segmentation, or edge detection steps, and it can be written as a sequence of denoising operations. We show that the region types we can effectively estimate in a mean-squared error sense are those for which the given transform provides a close approximation using sparse nonlinear approximants. We show the nature of the constructed estimators and how these estimators relate to the utilized transform and its sparsity over regions of interest. The developed estimation framework is general, and can readily be applied to other nonstationary signals with a suitable choice of linear transforms. Part I discusses fundamental issues, and Part II is devoted to adaptive algorithms with extensive simulation examples that demonstrate the power of the proposed techniques.

An Adaptive Strategy for Quality-Based Data Reduction in Wireless Sensor Networks

Abstract: Wireless sensor networks allow fine-grained observations of real-world phenomena. However, providing constant measurement updates incurs high communication costs for each individual node, resulting in increased energy depletion in the network. Data reduction strategies aim at reducing the amount of data sent by each node, for example by predicting the measured values both at the source and the sink node, thus only requiring nodes to send the readings that deviate from the prediction. While effectively reducing power consumption, such techniques so far needed to rely on a-priori knowledge to correctly model the expected values. Our approach instead employs an algorithm that requires no prior modeling, allowing nodes to work independently and without using global model parameters. Using the LeastMean-Square (LMS) adaptive algorithm on a publicly available, real-world (office environment) temperature data set, we have been able to achieve up to 92% communication reduction while maintaining a minimal accuracy of 0.5 degree Celsius.

Proportionate adaptive algorithms for network echo cancellation

Abstract: By analyzing the coefficient adaptation process of the steepest descent algorithm, the condition under which the fastest overall convergence will be achieved is obtained and the way to calculate optimal step-size control factors to satisfy that condition is formulated. Motivated by the results and using the stochastic approximation paradigm, the /spl mu/-law PNLMS (MPNLMS) algorithm is proposed to keep, in contrast to the proportionate normalized least-mean-square (PNLMS) algorithm, the fast initial convergence during the whole adaptation process in the case of sparse echo path identification. Modifications of the MPNLMS algorithm are proposed to lower the computational complexity.

Optimized parallel tempering simulations of proteins.

Abstract: We apply a recently developed adaptive algorithm that systematically improves the efficiency of parallel tempering or replica exchange methods in the numerical simulation of small proteins. Feedback iterations allow us to identify an optimal set of temperatures/replicas which are found to concentrate at the bottlenecks of the simulations. A measure of convergence for the equilibration of the parallel tempering algorithm is discussed. We test our algorithm by simulating the 36-residue villin headpiece subdomain HP-36 where we find a lowest-energy configuration with a root-mean-square deviation of less than 4 A to the experimentally determined structure.

Optimized parallel tempering simulations of proteins

Abstract: We apply a recently developed adaptive algorithm that systematically improves the efficiency of parallel tempering or replica exchange methods in the numerical simulation of small proteins. Feedback iterations allow us to identify an optimal set of temperatures/replicas which are found to concentrate at the bottlenecks of the simulations. A measure of convergence for the equilibration of the parallel tempering algorithm is discussed. We test our algorithm by simulating the 36-residue villin headpiece subdomain HP-36 where we find a lowest-energy configuration with a root-mean-square deviation of less than 4 A to the experimentally determined structure. © 2006 American Institute of Physics. DOI: 10.1063/1.2186639

Space-time adaptive processing: a knowledge-based perspective for airborne radar

Abstract: This article provides a brief review of radar space-time adaptive processing (STAP) from its inception to state-of-the art developments. The topic is treated from both intuitive and theoretical aspects. A key requirement of STAP is knowledge of the spectral characteristics underlying the interference scenario of interest. Additional issues of importance in STAP include the computational cost of the adaptive algorithm as well as the ability to maintain a constant false alarm rate (CFAR) over widely varying interference statistics. This article addresses these topics, developing the need for a knowledge-based (KB) perspective. The focus here is on signal processing for radar systems using multiple antenna elements that coherently process multiple pulses. An adaptive array of spatially distributed sensors, which processes multiple temporal snapshots, overcomes the directivity and resolution limitations of a single sensor.

Asmooth: a simple and efficient algorithm for adaptive kernel smoothing of two-dimensional imaging data

Abstract: An efficient algorithm for adaptive kernel smoothing (AKS) of two-dimensional imaging data has been developed and implemented using the Interactive Data Language (idl). The functional form of the kernel can be varied (top-hat, Gaussian, etc.) to allow different weighting of the event counts registered within the smoothing region. For each individual pixel, the algorithm increases the smoothing scale until the signal-to-noise ratio (S/N) within the kernel reaches a pre-set value. Thus, noise is suppressed very efficiently, while at the same time real structure, that is, signal that is locally significant at the selected S/N level, is preserved on all scales. In particular, extended features in noise-dominated regions are visually enhanced. The asmooth algorithm differs from other AKS routines in that it allows a quantitative assessment of the goodness of the local signal estimation by producing adaptively smoothed images in which all pixel values share the same S/N above the background. We apply asmooth to both real observational data (an X-ray image of clusters of galaxies obtained with the Chandra X-ray Observatory) and to a simulated data set. We find the asmoothed images to be fair representations of the input data in the sense that the residuals are consistent with pure noise, that is, they possess Poissonian variance and a near-Gaussian distribution around a mean of zero, and are spatially uncorrelated.

Nonlinear approximation based image recovery using adaptive sparse reconstructions and iterated denoising-part II: adaptive algorithms

Abstract: We combine the main ideas introduced in Part I with adaptive techniques to arrive at a powerful algorithm that estimates missing data in nonstationary signals. The proposed approach operates automatically based on a chosen linear transform that is expected to provide sparse decompositions over missing regions such that a portion of the transform coefficients over missing regions are zero or close to zero. Unlike prevalent algorithms, our method does not necessitate any complex preconditioning, segmentation, or edge detection steps, and it can be written as a progression of denoising operations. We show that constructing estimates based on nonlinear approximants is fundamentally a nonconvex problem and we propose a progressive algorithm that is designed to deal with this issue directly. The algorithm is applied to images through an extensive set of simulation examples, primarily on missing regions containing textures, edges, and other image features that are not readily handled by established estimation and recovery methods. We discuss the properties required of good transforms, and in conjunction, show the types of regions over which well-known transforms provide good predictors. We further discuss extensions of the algorithm where the utilized transforms are also chosen adaptively, where unpredictable signal components in the progressions are identified and not predicted, and where the prediction scenario is more general.

Adaptive Techniques for MIMO Radar

Abstract: By transmitting independent waveforms via different antennas, the echoes due to targets at different locations are linearly independent of each other, which allows the direct application of many adaptive techniques to achieve high resolution and excellent interference rejection capability. In the absence of array calibration errors, we discuss the application of several adaptive algorithms including Capon, APES and CAPES. When array errors are present, we apply the robust Capon beamformer (RCB) and doubly constrained robust Capon beamformer (DCRCB) approaches to the MIMO radar system to achieve accurate parameter estimation and superior interference and jamming suppression performance

MultiScale Modeling of Physical Phenomena: Adaptive Control of Models

Abstract: It is common knowledge that the accuracy with which computer simulations can depict physical events depends strongly on the choice of the mathematical model of the events. Perhaps less appreciated is the notion that the error due to modeling can be defined, estimated, and used adaptively to control modeling error, provided one accepts the existence of a base model that can serve as a datum with respect to which other models can be compared. In this work, it is shown that the idea of comparing models and controlling model error can be used to develop a general approach for multiscale modeling, a subject of growing importance in computational science. A posteriori estimates of modeling error in so-called quantities of interest are derived and a class of adaptive modeling algorithms is presented. Several applications of the theory and methodology are presented. These include preliminary work on random multiphase composite materials, molecular statics simulations with applications to problems in nanoindentation, and analysis of molecular dynamics models using various techniques for scale bridging.

Further Study on Robust Adaptive Beamforming With Optimum Diagonal Loading

Abstract: Significant effort has gone into designing robust adaptive beamforming algorithms to improve robustness against uncertainties in array manifold. These uncertainties may be caused by uncertainty in direction-of-arrival (DOA), imperfect array calibration, near-far effect, mutual coupling, and other mismatch and modeling errors. A diagonal loading technique is obligatory to fulfil the uncertainty constraint where the diagonal loading level is amended to satisfy the constrained value. The major drawback of diagonal loading techniques is that it is not clear how to get the optimum value of diagonal loading level based on the recognized level of uncertainty constraint. In this paper, an alternative realization of the robust adaptive linearly constrained minimum variance beamforming with ellipsoidal uncertainty constraint on the steering vector is developed. The diagonal loading technique is integrated into the adaptive update schemes by means of optimum variable loading technique which provides loading-on-demand mechanism rather than fixed, continuous or ad hoc loading. We additionally enrich the proposed robust adaptive beamformers by imposing a cooperative quadratic constraint on the weight vector norm to overcome noise enhancement at low SNR. Several numerical simulations with DOA mismatch, moving jamming, and mutual coupling are carried out to explore the performance of the proposed schemes and compare their performance with other traditional and robust beamformers

Removal of ocular artifacts from EEG using adaptive thresholding of wavelet coefficients

Abstract: Electroencephalogram (EEG) gives researchers a non-invasive way to record cerebral activity. It is a valuable tool that helps clinicians to diagnose various neurological disorders and brain diseases. Blinking or moving the eyes produces large electrical potential around the eyes known as electrooculogram. It is a non-cortical activity which spreads across the scalp and contaminates the EEG recordings. These contaminating potentials are called ocular artifacts (OAs). Rejecting contaminated trials causes substantial data loss, and restricting eye movements/blinks limits the possible experimental designs and may affect the cognitive processes under investigation. In this paper, a nonlinear time-scale adaptive denoising system based on a wavelet shrinkage scheme has been used for removing OAs from EEG. The time-scale adaptive algorithm is based on Stein's unbiased risk estimate (SURE) and a soft-like thresholding function which searches for optimal thresholds using a gradient based adaptive algorithm is used. Denoising EEG with the proposed algorithm yields better results in terms of ocular artifact reduction and retention of background EEG activity compared to non-adaptive thresholding methods and the JADE algorithm.

Predicting Chaotic Time Series Using Neural and Neurofuzzy Models: A Comparative Study

Abstract: The prediction accuracy and generalization ability of neural/neurofuzzy models for chaotic time series prediction highly depends on employed network model as well as learning algorithm. In this study, several neural and neurofuzzy models with different learning algorithms are examined for prediction of several benchmark chaotic systems and time series. The prediction performance of locally linear neurofuzzy models with recently developed Locally Linear Model Tree (LoLiMoT) learning algorithm is compared with that of Radial Basis Function (RBF) neural network with Orthogonal Least Squares (OLS) learning algorithm, MultiLayer Perceptron neural network with error back-propagation learning algorithm, and Adaptive Network based Fuzzy Inference System. Particularly, cross validation techniques based on the evaluation of error indices on multiple validation sets is utilized to optimize the number of neurons and to prevent over fitting in the incremental learning algorithms. To make a fair comparison between neural and neurofuzzy models, they are compared at their best structure based on their prediction accuracy, generalization, and computational complexity. The experiments are basically designed to analyze the generalization capability and accuracy of the learning techniques when dealing with limited number of training samples from deterministic chaotic time series, but the effect of noise on the performance of the techniques is also considered. Various chaotic systems and time series including Lorenz system, Mackey-Glass chaotic equation, Henon map, AE geomagnetic activity index, and sunspot numbers are examined as case studies. The obtained results indicate the superior performance of incremental learning algorithms and their respective networks, such as, OLS for RBF network and LoLiMoT for locally linear neurofuzzy model.

Adaptive wavelet neural network control with hysteresis estimation for piezo-positioning mechanism

Abstract: An adaptive wavelet neural network (AWNN) control with hysteresis estimation is proposed in this study to improve the control performance of a piezo-positioning mechanism, which is always severely deteriorated due to hysteresis effect. First, the control system configuration of the piezo-positioning mechanism is introduced. Then, a new hysteretic model by integrating a modified hysteresis friction force function is proposed to represent the dynamics of the overall piezo-positioning mechanism. According to this developed dynamics, an AWNN controller with hysteresis estimation is proposed. In the proposed AWNN controller, a wavelet neural network (WNN) with accurate approximation capability is employed to approximate the part of the unknown function in the proposed dynamics of the piezo-positioning mechanism, and a robust compensator is proposed to confront the lumped uncertainty that comprises the inevitable approximation errors due to finite number of wavelet basis functions and disturbances, optimal parameter vectors, and higher order terms in Taylor series. Moreover, adaptive learning algorithms for the online learning of the parameters of the WNN are derived based on the Lyapunov stability theorem. Finally, the command tracking performance and the robustness to external load disturbance of the proposed AWNN control system are illustrated by some experimental results.

Adaptive stabilizing and tracking control for a nonlinear inverted-pendulum system via sliding-mode technique

Abstract: Since the system behaviors of a dual-axis inverted-pendulum mechanism including actuator dynamics are highly nonlinear, it is difficult to design a suitable control system that realizes real-time stabilization and accurate tracking control at all times. In this paper, an adaptive sliding-mode control system is implemented to control a dual-axis inverted-pendulum mechanism that is driven by permanent magnet synchronous motors. First, the energy conservation principle is adopted to build a mathematical model of the motor-mechanism-coupled system. Moreover, an adaptive sliding-mode control system is developed for stabilizing and tracking control of the dual-axis inverted-pendulum system, where an adaptive algorithm is investigated to relax the requirement of the bound of lumped uncertainty in the traditional sliding-mode control. In addition, numerical simulation and experimental results show that the proposed control scheme provides high-performance dynamic characteristics and is robust with regard to parametric variations, various reference trajectories, and different initial states.

A New Adaptive Backpropagation Algorithm Based on Lyapunov Stability Theory for Neural Networks

Abstract: A new adaptive backpropagation (BP) algorithm based on Lyapunov stability theory for neural networks is developed in this paper. It is shown that the candidate of a Lyapunov function V(k) of the tracking error between the output of a neural network and the desired reference signal is chosen first, and the weights of the neural network are then updated, from the output layer to the input layer, in the sense that DeltaV(k)=V(k)-V(k-1)<0. The output tracking error can then asymptotically converge to zero according to Lyapunov stability theory. Unlike gradient-based BP training algorithms, the new Lyapunov adaptive BP algorithm in this paper is not used for searching the global minimum point along the cost-function surface in the weight space, but it is aimed at constructing an energy surface with a single global minimum point through the adaptive adjustment of the weights as the time goes to infinity. Although a neural network may have bounded input disturbances, the effects of the disturbances can be eliminated, and asymptotic error convergence can be obtained. The new Lyapunov adaptive BP algorithm is then applied to the design of an adaptive filter in the simulation example to show the fast error convergence and strong robustness with respect to large bounded input disturbances

A fast adaptive motion estimation algorithm

Abstract: Motion estimation (ME) is a multistep process that involves not one, but a combination of techniques, such as motion starting point, motion search patterns, and adaptive control to curb the search, avoidance of search stationary regions, etc. The collective efficiency of these techniques is what makes a ME algorithm robust and efficient across the board. This paper proposes a ME algorithm that is an embodiment of several effective ideas for finding the most accurate motion vectors (MVs) with the aim to maximize the encoding speed as well as the visual quality. The proposed algorithm takes advantage of the correlation between MVs in both spatial and temporal domains, controls to curb the search, avoids of search stationary regions, and uses switchable shape search patterns to accelerate motion search. The algorithm yields very similar quality compared to the full search but with several hundred times faster speed. We have evaluated the algorithm through a comprehensive performance study that shows that the proposed algorithm achieves substantial speedup without quality loss for a wide range of video sequences, compared with the ME techniques recommended by the MPEG-4 committee.

Error reduction and convergence for an adaptive mixed finite element method

Abstract: An adaptive mixed finite element method (AMFEM) is designed to guarantee an error reduction, also known as saturation property: after each refinement step, the error for the fine mesh is strictly smaller than the error for the coarse mesh up to oscillation terms. This error reduction property is established here for the Raviart-Thomas finite element method with a reduction factor p < 1 uniformly for the L 2 norm of the flux errors. Our result allows for linear convergence of a proper adaptive mixed finite element algorithm with respect to the number of refinement levels. The adaptive algorithm surprisingly does not require any particular mesh design, unlike the conforming finite element method. The new arguments are a discrete local efficiency and a quasi-orthogonality estimate. The proof does not rely on duality or on regularity.

Super-Gaussian mixture source model for ICA

Abstract: We propose an extension of the mixture of factor (or independent component) analyzers model to include strongly super-gaussian mixture source densities. This allows greater economy in representation of densities with (multiple) peaked modes or heavy tails than using several Gaussians to represent these features. We derive an EM algorithm to find the maximum likelihood estimate of the model, and show that it converges globally to a local optimum of the actual non-gaussian mixture model without needing any approximations. This extends considerably the class of source densities that can be used in exact estimation, and shows that in a sense super-gaussian densities are as natural as Gaussian densities. We also derive an adaptive Generalized Gaussian algorithm that learns the shape parameters of Generalized Gaussian mixture components. Experiments verify the validity of the algorithm.