Showing papers on "Adaptive algorithm published in 1995"

Local bisection refinement for N -simplicial grids generated by reflection

Abstract: A simple local bisection refinement algorithm for the adaptive refinement of n-simplicial grids is presented. The algorithm requires that the vertices of each simplex be ordered in a special way re...

A robust variable step size LMS-type algorithm: analysis and simulations

Abstract: The paper presents a robust variable step size LMS-type algorithm with the attractive property of achieving a small final misadjustment while providing fast convergence at early stages of adaptation. The performance of the algorithm is not affected by the presence of noise. Approximate analysis of convergence and steady state performance for zero-mean stationary Gaussian inputs and a nonstationary optimal weight vector is provided. Simulation results clearly indicate its superior performance for stationary cases. For the nonstationary environment, the algorithm provides performance equivalent to that of the regular LMS algorithm.

Optimal adaptive k-means algorithm with dynamic adjustment of learning rate

Abstract: Adaptive k-means clustering algorithms have been used in several artificial neural network architectures, such as radial basis function networks or feature-map classifiers, for a competitive partitioning of the input domain. This paper presents an enhancement of the traditional k-means algorithm. It approximates an optimal clustering solution with an efficient adaptive learning rate, which renders it usable even in situations where the statistics of the problem task varies slowly with time. This modification Is based on the optimality criterion for the k-means partition stating that: all the regions in an optimal k-means partition have the same variations if the number of regions in the partition is large and the underlying distribution for generating input patterns is smooth. The goal of equalizing these variations is introduced in the competitive function that assigns each new pattern vector to the "appropriate" region. To evaluate the optimal k-means algorithm, the authors first compare it to other k-means variants on several simple tutorial examples, then the authors evaluate it on a practical application: vector quantization of image data. >

Bayesian algorithms for adaptive change detection in image sequences using Markov random fields

Abstract: In many conventional methods for change detection, the detections are carried out by comparing a test statistic, which is computed locally for each location on the image grid, with a global threshold. These ‘nonadaptive’ methods for change detection suffer from the dilemma of either causing many false alarms or missing considerable parts of non-stationary areas. This contribution presents a way out of this dilemma by viewing change detection as an inverse, ill-posed problem. As such, the problem can be solved using prior knowledge about typical properties of change masks. This reasoning leads to a Bayesian formulation of change detection, where the prior knowledge is brought to bear by appropriately specified a priori probabilities. Based on this approach, a new, adaptive algorithm for change detection is derived where the decision thresholds vary depending on context, thus improving detection performance substantially. The algorithm requires only a single raster scan per picture and increases the computional load only slightly in comparison to non-adaptive techniques.

Algorithms for constrained molecular dynamics

Abstract: In molecular dynamics simulations, the fastest components of the potential field impose severe restrictions on the stability and hence the speed of computational methods. One possibility for treating this problem is to replace the fastest components with algebraic length constraints. In this article the resulting systems of mixed differential and algebraic equations are studied. Commonly used discretization schemes for constrained Hamiltonian systems are discussed. The form of the nonlinear equations is examined in detail and used to give convergence results for the traditional nonlinear solution technique SHAKE iteration and for a modification based on successive overrelaxation (SOR). A simple adaptive algorithm for finding the optimal relaxation parameter is presented. Alternative direct methods using sparse matrix techniques are discussed. Numerical results are given for the new techniques, which have been implemented in the molecular modeling software package CHARMM and show as much as twofold improvement over SHAKE iteration. © 1995 John Wiley & Sons, Inc.

Active control of vibration using a neural network

Abstract: Feedforward control of sound and vibration using a neural network-based control system is considered, with the aim being to derive an architecture/algorithm combination which is capable of supplanting the commonly used finite impulse response filter/filtered-x least mean square (LMS) linear arrangement for certain nonlinear problems. An adaptive algorithm is derived which enables stable adaptation of the neural controller for this purpose, while providing the capacity to maintain causality within the control scheme. The algorithm is shown to be simply a generalization of the linear filtered-x LMS algorithm. Experiments are undertaken which demonstrate the utility of the proposed arrangement, showing that it performs as well as a linear control system for a linear control problem and better for a nonlinear control problem. The experiments also lead to the conclusion that more work is required to improve the predictability and consistency of the performance before the neural network controller becomes a practical alternative to the current linear feedforward systems. >

Method and apparatus for dynamic sound optimization

Abstract: This invention comprises a system to compensate for the noise level inside a vehicle by measuring the music level and the noise level in the vehicle through the use of analog to digital conversion and adaptive digital filtering, including a sensing microphone in the vehicle cabin to measure both music and noise; preamplification and analog to digital (A/D) conversion of the microphone signal; A/D conversion of a stereo music signal; a pair of filters that use an adaptive algorithm such as the known Least Mean Squares ("LMS") method to extract the noise from the total cabin sound; an estimation of the masking effect of the noise on the music; an adaptive correction of the music loudness and, optionally, equalization to overcome the masking effect; digital to analog (D/A) conversion of the corrected music signal; and transmission of the corrected music signal to the audio system.

Matching pursuit of images

Abstract: A crucial problem in image analysis is to construct efficient low-level representations of an image, providing precise characterization of features which compose it, such as edges and texture components. An image usually contains very different types of features, which have been successfully modelled by the very redundant family of 2D Gabor oriented wavelets, describing the local properties of the image: localization, scale, preferred orientation, amplitude and phase of the discontinuity. However, this model generates representations of very large size. Instead of decomposing a given image over this whole set of Gabor functions, we use an adaptive algorithm (called matching pursuit) to select the Gabor elements which approximate at best the image, corresponding to the main features of the image. This produces compact representation in terms of few features that reveal the local image properties. Results proved that the elements are precisely localized on the edges of the images, and give a local decomposition as linear combinations of "textons" in the textured regions. We introduce a fast algorithm to compute the matching pursuit decomposition.

Analysis of adaptive step-size SA algorithms for parameter tracking

Abstract: We present proofs and data for adaptive step-size algorithms for tracking time-varying parameters when recursive stochastic approximation type algorithms are used. A classical problem in adaptive control and communication theory concerns the tracking of the best fit of a given form when the statistics or the parameters change slowly. A major, and yet unresolved, problem has been the choice of the step sizes in the tracking algorithm. An algorithm for adapting the step size using the same system measurements which are used for the tracking was suggested by Benveniste and various examples worked out by Brossier. The numerical results were very encouraging. But proofs were lacking. These proofs are supplied here together with supporting numerical data. The proofs are based on recent results in stochastic approximation. The adaptive step-size technique works very well indeed. Much supporting analysis is presented, particularly concerning the interpretation of certain stationary processes as "stationary" pathwise derivatives. Finite difference forms are also treated. These are mathematically simpler and can be applied to a wide variety of systems, even when the system is not well modeled. The data shows that they work well. >

A posteriori estimation and adaptive control of the pollution error in the h‐version of the finite element method

Abstract: : We studied the pollution-error in the h-version of the finite element method and its effect on the quality of the local error indicators (resp. the quality of the derivatives recovered by local postprocessing) in the interior of the mesh. Here we show that it is possible to construct a-posteriori estimates of the pollution-error in a patch of elements by employing the local error indicators over the entire mesh. We also give an adaptive algorithm for the local control of the pollution-error in a patch of elements of interest.

Adaptive receiver algorithms for near-far resistant CDMA

Abstract: Adaptive receiver algorithms are considered for the demodulation of code-division multiple-access (CDMA) signals. These algorithms include neural-network based algorithms and algorithms adapted from linear channel equalization techniques. Convergence issues are treated, and the performance of various algorithms is compared via computer simulations. >

Comparison of DC offset effects in four LMS adaptive algorithms

Abstract: It is well known that DC offsets degrade the performance of analog adaptive filters. In this paper, the effects of DC offsets on four variations of the stochastic gradient algorithm are analyzed. Assuming a Gaussian probability distribution for the input signal and error signal, the output mean squared error (MSE) performance in the presence of DC offsets is evaluated for each of the algorithms. The theoretical work is compared with computer simulations and the results, together with convergence properties of each of the algorithms and their respective hardware requirements, are used in selecting the most appropriate algorithm. Although a Gaussian input distribution is assumed, it may reasonably be inferred that the critical results obtained should also hold for other input distributions. >

Adaptive marching cubes

Abstract: The marching cubes algorithm (MC) is a powerful technique for surface rendering that can produce very high-quality images. However, it is not suitable for interactive manipulation of the 3D surfaces constructed from high-resolution volume datasets in terms of both space and time. In this paper, we present an adaptive version of MC called adaptive marching cubes (AMC). It significantly reduces the number of triangles representing the surface by adapting the size of the triangles to the shape of the surface. This improves the performance of the manipulation of the 3D surfaces. A typical example with the volume dataset of size 256×256×113 shows that the number of triangles is reduced by 55%. The quality of images produced by AMC is similar to that of MC. One of the fundamental problems encountered with adaptive algorithms is thecrack problem. Cracks may be created between two neighboring cubes processed with different levels of subdivision. We solve the crack problem by patching the cracks using polygons of the smae shape as those of the cracks. We propose a simple, but complete, method by first abstracting 22 basic configurations of arbitrarily sized cracks and then reducing the handling of these configurations to a simple rule. It requires onlyO(n 2) working memory for an×n×n volume data set.

Approximation of discrete-time state-space trajectories using dynamic recurrent neural networks

Abstract: In this note, the approximation capability of a class of discrete-time dynamic recurrent neural networks (DRNN's) is studied. Analytical results presented show that some of the states of such a DRNN described by a set of difference equations may be used to approximate uniformly a state-space trajectory produced by either a discrete-time nonlinear system or a continuous function on a closed discrete-time interval. This approximation process, however, has to be carried out by an adaptive learning process. This capability provides the potential for applications such as identification and adaptive control. >

The generalized multidelay adaptive filter: structure and convergence analysis

Abstract: Frequency-domain adaptive filters have long been recognized as an attractive alternative to time-domain algorithms when dealing with systems with large impulse response and/or correlated input. New frequency-domain LMS adaptive schemes have been proposed. These algorithms essentially retain the attractive features of frequency-domain implementations, while requiring a processing delay considerably smaller than the length of the impulse response. The authors show that these algorithms can be seen as particular implementations of a more general scheme, the generalized multidelay filter (GMDF). Within this general class of algorithms, we focus on implementations based on the weighted overlap and add reconstruction algorithms; these variants, overlooked in previous contributions, provide an independent control of the overall processing delay and of the rate of update of the filter coefficients, allowing a trade-off between the computational complexity and the rate of convergence. We present a comprehensive analysis of the performance of this new scheme and to provide insight into the influence of impulse response segmentation on the behavior of the adaptive algorithm. Exact analytical expressions for the steady-state mean-square error are first derived. Necessary and sufficient conditions for the convergence of the algorithm to the optimal solution within finite variance are then obtained, and are translated into bounds for the stepsize parameter. Simulations are presented to support our analysis and to demonstrate the practical usefulness of the GMDF algorithm in applications where large impulse response has to be processed. >

A robust adaptive controller for PM motor drives in robotic applications

Abstract: The paper deals with theoretical development and practical implementation of an adaptive speed and position regulator suitable for robotic applications. The proposed adaptive control scheme is characterized by a reduced amount of computation and is based on the model reference adaptive control approach to compensate the variations of the system parameters, such as inertia and torque constant. A disturbance torque observer is employed to balance the required load torque and reduce the complexity of the adaptive algorithm. Simulation tests of a robotic drive, including an interior type permanent magnet synchronous (IPMS) motor, are reported in order to compare the proposed control scheme with standard speed and position regulators. Experimental results, obtained from a prototype based on a commercial PC board, are also reported in order to practically evaluate the feasibility and the features of the proposed adaptive control scheme. >

Receiver-oriented adaptive buffer allocation in credit-based flow control for ATM networks

Abstract: In credit-based flow control for ATM networks, a buffer is first allocated to each VC (virtual circuit) and then credit control is applied to the VC for avoiding possible buffer overflow. Receiver-oriented, adaptive buffer allocation allows a receiver to allocate its buffer dynamically, to VCs from multiple upstream nodes based on their bandwidth usage. The paper describes, in detail, such an adaptive algorithm capable of supporting a wide range of link speeds and propagation delays, and also packing multiple allocation and credit records in a single message. Analysis and simulation results show that even under highly bursty traffic, the adaptive scheme guarantees no cell loss due to congestion, and achieves excellent performance in utilization, fairness, ramp-up and packing, while requiring only relatively small node memory and bandwidth overhead. The required memory need only be 4*RTT+2*N, where RTT is the link round-trip time in cell cycles and N is the number of VCs.

Adaptive interacting multiple model algorithm for tracking a manoeuvring target

Abstract: The paper describes an adaptive interacting multiple-model algorithm (AIMM) for use in manoeuvring target tracking. The algorithm does not need predefined models. A two-stage Kalman estimator is used to estimate the acceleration of the target. This acceleration value is then fed to the subfilters in an interacting multiple-model (IMM) algorithm, where the subfilters have different acceleration parameters. Results compare the performance of the AIMM algorithm with the IMM algorithm, using simulations of different manoeuvring-target scenarios. Also considered are the relative computational requirements, and the ease with which the algorithms can be implemented on parallel machines.

Methods to speed up error back-propagation learning algorithm

Abstract: Error back propagation (EBP) is now the most used training algorithm for feedforward artificial neural networks (FFANNs). However, it is generally believed that it is very slow if it does converge, especially if the network size is not too large compared to the problem at hand. The main problem with the EBP algorithm is that it has a constant learning rate coefficient, and different regions of the error surface may have different characteristic gradients that may require a dynamic change of learning rate coefficient based on the nature of the surface. Also, the characteristic of the error surface may be unique in every dimension, which may require one learning rate coefficient for each weight. To overcome these problems several modifications have been suggested. This survey is an attempt to present them together and to compare them. The first modification was momentum strategy where a fraction of the last weight correction is added to the currently suggested weight correction. It has both an accelerating and a decelerating effect where they are necessary. However, this method can give only a relatively small dynamic range for the learning rate coefficient. To increase the dynamic range of the learning rate coefficient, such methods as the bold driver and SAB (self-adaptive back propagation) were proposed. A modification to the SAB that eliminates the requirement of selection of a good learning rate coefficient by the user gave the SuperSAB. A slight modification to the momentum strategy produced a new method that controls the oscillation of weights to speed up learning. Modification to the EBP algorithm in which the gradients are rescaled at every layer helped to improve the performance. Use of expected output of a neuron instead of actual output for correcting weights improved performance of the momentum strategy. The conjugate gradient method and self-determination of adaptive learning rate require no learning rate coefficient from the user. Use of energy functions other than the sum of the squared error has shown improved convergence rate. An effective learning rate coefficient selection needs to consider the size of the training set. All these methods to improve the performance of the EBP algorithm are presented here.

Adaptive filtering of stable processes for active attenuation of impulsive noise

Abstract: Describes a new class of algorithms for active noise control (ANC) for use in environments in which impulsive noise is present. The well known filtered-X and filtered-U ANC algorithms are designed to minimize the variance of a measured error signal. For impulsive noise, which can be modeled using non-Gaussian stable processes, these standard approaches are not appropriate since the second order moments do not exist. The authors propose a new class of adaptive algorithms for ANC that are based on the minimization of a fractional lower order moment, p<2. By studying the effect of p on the convergence behavior of adaptive algorithms, they observe that superior performance is obtained by choosing p/spl ap//spl alpha/ where /spl alpha/<2 is a parameter reflecting the degree of impulsiveness of the noise. Applications of this approach to noise cancellation in a duct are presented.

Adaptive Bayesian decision feedback equalizer for dispersive mobile radio channels

Abstract: The paper investigates adaptive equalization of time-dispersive mobile radio fading channels and develops a robust high performance Bayesian decision feedback equalizer (DFE). The characteristics and implementation aspects of this Bayesian DFE are analyzed, and its performance is compared with those of the conventional symbol or fractional spaced DFE and the maximum likelihood sequence estimator (MLSE). In terms of computational complexity, the adaptive Bayesian DFE is slightly more complex than the conventional DFE but is much simpler than the adaptive MLSE. In terms of error rate in symbol detection, the adaptive Bayesian DFE outperforms the conventional DFE dramatically. Moreover, for severely fading multipath channels, the adaptive MLSE exhibits significant degradation from the theoretical optimal performance and becomes inferior to the adaptive Bayesian DFE. >

Optimal tracking of time-varying channels: a frequency domain approach for known and new algorithms

Abstract: In this paper, we developed a systematic frequency domain approach to analyze adaptive tracking algorithms for fast time-varying channels. The analysis is performed with the help of two new concepts, a tracking filter and a tracking error filter, which are used to calculate the mean square identification error (MSIE). First, we analyze existing algorithms, the least mean squares (LMS) algorithm, the exponential windowed recursive least squares (EW-RLS) algorithm and the rectangular windowed recursive least squares (RW-RLS) algorithm. The equivalence of the three algorithms is demonstrated by employing the frequency domain method. A unified expression for the MSIE of all three algorithms is derived. Secondly, we use the frequency domain analysis method to develop an optimal windowed recursive least squares (OW-RLS) algorithm. We derive the expression for the MSIE of an arbitrary windowed RLS algorithm and optimize the window shape to minimize the MSIE. Compared with an exponential window having an optimized forgetting factor, an optimal window results in a significant improvement in the h MSIE. Thirdly, we propose two types of robust windows, the average robust window and the minimax robust window. The RLS algorithms designed with these windows have near-optimal performance, but do not require detailed statistics of the channel. >

Adaptive frequency sampling algorithm for fast and accurate S-parameter modeling of general planar structures

Abstract: A new adaptive technique is proposed to represent the spectral response of general planar structures over some frequency range of interest with a minimal number of frequency samples. Rational fitting functions are used to model and interpolate the S-parameters obtained through electromagnetic simulation. The adaptive algorithm doesn't require any a priori knowledge of the dynamics of the S-parameters in order to select an appropriate sampling distribution. This greatly improves the transparent use of any electromagnetic simulator. >

A sliding mode strategy for adaptive learning in Adalines

Abstract: A dynamical sliding mode control approach is proposed for robust adaptive learning in analog Adaptive Linear Elements (Adalines), constituting basic building blocks for perceptron-based feedforward neural networks. The zero level set of the learning error variable is regarded as a sliding surface in the space of learning parameters. A sliding mode trajectory can then be induced, in finite time, on such a desired sliding manifold. Neuron weights adaptation trajectories are shown to be of continuous nature, thus avoiding bang-bang weight adaptation procedures. Sliding mode invariance conditions determine a least squares characterization of the adaptive weights average dynamics whose stability features may be studied using standard time-varying linear systems results. Robustness of the adaptative learning algorithm, with respect to bounded external perturbation signals, and measurement noises, is also demonstrated. The article presents some simulation examples dealing with applications of the proposed algorithm to forward and inverse plant dynamics identification.

Design of adaptive feedforward algorithms using internal model equivalence

Abstract: The paper investigates the design of adaptive feedforward cancellation (AFC) algorithms with sinusoidal regressors for repetitive control. Such adaptive algorithms are equivalent to linear controllers based on the internal model principle (IMP). Using this equivalence and root locus rules, the phase advance of the regressor of the adaptive algorithm can be chosen to maximize the phase margin at low gains. A surprising result is that selecting the optimal phase advance is equivalent to placing a zero in the open right half-plane in certain cases. A complete design and analysis for the compensation of a single-frequency periodic disturbance is done. A new variation of the AFC algorithm is also developed in which the adaptive portion acts in parallel with a feedthrough term. the IMP equivalent of this algorithm has two zeros instead of one. Analysis and simulation show this method to have superior convergence and robustness properties when compared with the method having no feedthrough term. Discrete time versions of the algorithms are briefly considered.

A set of algorithms linking NLMS and block RLS algorithms

Abstract: This paper describes a set of block processing algorithms which contains as extremal cases the normalized least mean squares (NLMS) and the block recursive least squares (BRLS) algorithms. All these algorithms use small block lengths, thus allowing easy implementation and small input-output delay. It is shown that these algorithms require a lower number of arithmetic operations than the classical least mean squares (LMS) algorithm, while converging much faster. A precise evaluation of the arithmetic complexity is provided, and the adaptive behavior of the algorithm is analyzed. Simulations illustrate that the tracking characteristics of the new algorithm are also improved compared to those of the NLMS algorithm. The conclusions of the theoretical analysis are checked by simulations, illustrating that, even in the case where noise is added to the reference signal, the proposed algorithm allows altogether a faster convergence and a lower residual error than the NLMS algorithm. Finally, a sample-by-sample version of this algorithm is outlined, which is the link between the NLMS and recursive least squares (RLS) algorithms. >

Adaptive estimation of eigensubspace

Abstract: In a recent work we recast the problem of estimating the minimum eigenvector (eigenvector corresponding to the minimum eigenvalue) of a symmetric positive definite matrix into a neural network framework. We now extend this work using an inflation technique to estimate all or some of the orthogonal eigenvectors of the given matrix. Based on these results, we form a cost function for the finite data case and derive a Newton-based adaptive algorithm. The inflation technique leads to a highly modular and parallel structure for implementation. The computational requirement of the algorithm is O(N/sup 2/), N being the size of the covariance matrix. We also present a rigorous convergence analysis of this adaptive algorithm. The algorithm is locally convergent and the undesired stationary points are unstable. Computer simulation results are provided to compare its performance with that of two adaptive subspace estimation methods proposed by Yang and Kaveh (1988) and an improved version of one of them, for stationary and nonstationary signal scenarios. The results show that the proposed approach performs identically to one of them and is significantly superior to the remaining two. >