Showing papers on "Adaptive algorithm published in 2000"
TL;DR: A modified XCS classifier system is described that learns a non-linear real-vector classification task.
Abstract: Classifier systems have traditionally taken binary strings as inputs, yet in many real problems such as data inference, the inputs have real components. A modified XCS classifier system is described that learns a non-linear real-vector classification task.
350 citations
TL;DR: In this paper, an adaptive algorithm for low-Mach number reacting flows with complex chemistry is presented, which uses a form of the low Mach number equations that discretely conserves both mass and energy.
Abstract: We present an adaptive algorithm for low Mach number reacting flows with complex chemistry. Our approach uses a form of the low Mach number equations that discretely conserves both mass and energy. The discretization methodology is based on a robust projection formulation that accommodates large density contrasts. The algorithm uses an operator-split treatment of stiff reaction terms and includes effects of differential diffusion. The basic computational approach is embedded in an adaptive projection framework that uses structured hierarchical grids with subcycling in time that preserves the discrete conservation properties of the underlying single-grid algorithm. We present numerical examples illustrating the performance of the method on both premixed and non-premixed flames.
282 citations
TL;DR: It is shown that this measure is not appropriate and a modified form that meets, in an optimal way, the needs for an efficient DTD is proposed that is also a definition of the normalized cross-correlation matrix between two vectors and a link with the coherence function.
Abstract: A doubletalk detector (DTD) is used with an echo canceler to sense when far-end speech is corrupted by near-end speech. Its role is to freeze the adaptation of the model filter when near-end speech is present in order to avoid divergence of the adaptive algorithm. Several authors have proposed to use the cross-correlation coefficient vector between the input signal vector x and the scalar output y for a DTD. We show in this paper that this measure is not appropriate and propose a modified form that meets, in an optimal way, the needs for an efficient DTD. By extension, we also propose a definition of the normalized cross-correlation matrix between two vectors and show a link with the coherence function.
247 citations
TL;DR: This work analyzes the convergence behavior of the generalized APA class of algorithms (allowing for arbitrary delay between input vectors) using a simple model for the input signal vectors and shows that the convergence rate is exponential and that it improves as the number of input signal vector used for adaptation is increased.
Abstract: A class of equivalent algorithms that accelerate the convergence of the normalized LMS (NLMS) algorithm, especially for colored inputs, has previously been discovered independently. The affine projection algorithm (APA) is the earliest and most popular algorithm in this class that inherits its name. The usual APA algorithms update weight estimates on the basis of multiple, unit delayed, input signal vectors. We analyze the convergence behavior of the generalized APA class of algorithms (allowing for arbitrary delay between input vectors) using a simple model for the input signal vectors. Conditions for convergence of the APA class are derived. It is shown that the convergence rate is exponential and that it improves as the number of input signal vectors used for adaptation is increased. However, the rate of improvement in performance (time-to-steady-state) diminishes as the number of input signal vectors increases. For a given convergence rate, APA algorithms are shown to exhibit less misadjustment (steady-state error) than NLMS. Simulation results are provided to corroborate the analytical results.
218 citations
TL;DR: An adaptive method of directly obtaining the inverse of the Fisher information matrix is proposed and it generalizes the adaptive Gauss-Newton algorithms and provides a solid theoretical justification of them.
Abstract: The natural gradient learning method is known to have ideal performances for on-line training of multilayer perceptrons. It avoids plateaus, which give rise to slow convergence of the backpropagation method. It is Fisher efficient, whereas the conventional method is not. However, for implementing the method, it is necessary to calculate the Fisher information matrix and its inverse, which is practically very difficult. This article proposes an adaptive method of directly obtaining the inverse of the Fisher information matrix. It generalizes the adaptive Gauss-Newton algorithms and provides a solid theoretical justification of them. Simulations show that the proposed adaptive method works very well for realizing natural gradient learning.
216 citations
TL;DR: This paper shows that the adaptive natural gradient method can be extended to be applicable to a wide class of stochastic models: regression with an arbitrary noise model and classification with an arbitrarily number of classes.
188 citations
TL;DR: This paper discusses a novel distributed adaptive algorithm and representation used to construct populations of adaptive Web agents that browse networked information environments on-line in search of pages relevant to the user, by traversing hyperlinks in an autonomous and intelligent fashion.
Abstract: This paper discusses a novel distributed adaptive algorithm and representation used to construct populations of adaptive Web agents. These InfoSpiders browse networked information environments on-line in search of pages relevant to the user, by traversing hyperlinks in an autonomous and intelligent fashion. Each agent adapts to the spatial and temporal regularities of its local context thanks to a combination of machine learning techniques inspired by ecological models: evolutionary adaptation with local selection, reinforcement learning and selective query expansion by internalization of environmental signals, and optional relevance feedback. We evaluate the feasibility and performance of these methods in three domains: a general class of artificial graph environments, a controlled subset of the Web, and (preliminarly) the full Web. Our results suggest that InfoSpiders could take advantage of the starting points provided by search engines, based on global word statistics, and then use linkage topology to guide their search on-line. We show how this approach can complement the current state of the art, especially with respect to the scalability challenge.
176 citations
TL;DR: An adaptive algorithm for blind source separation is derived, which is called the multiuser kurtosis (MUK) algorithm, which combines a stochastic gradient update and a Gram-Schmidt orthogonalization procedure in order to satisfy the criterion's whiteness constraints.
Abstract: We consider the problem of recovering blindly (i.e., without the use of training sequences) a number of independent and identically distributed source (user) signals that are transmitted simultaneously through a linear instantaneous mixing channel. The received signals are, hence, corrupted by interuser interference (IUI), and we can model them as the outputs of a linear multiple-input-multiple-output (MIMO) memoryless system. Assuming the transmitted signals to be mutually independent, i.i.d., and to share the same non-Gaussian distribution, a set of necessary and sufficient conditions for the perfect blind recovery (up to scalar phase ambiguities) of all the signals exists and involves the kurtosis as well as the covariance of the output signals. We focus on a straightforward blind constrained criterion stemming from these conditions. From this criterion, we derive an adaptive algorithm for blind source separation, which we call the multiuser kurtosis (MUK) algorithm. At each iteration, the algorithm combines a stochastic gradient update and a Gram-Schmidt orthogonalization procedure in order to satisfy the criterion's whiteness constraints. A performance analysis of its stationary points reveals that the MUK algorithm is free of any stable undesired local stationary points for any number of sources; hence, it is globally convergent to a setting that recovers them all.
176 citations
TL;DR: In this paper, a simple adaptive approach to optimize seat protection levels in airline revenue management systems is proposed, which uses historical observations of the relative frequencies of certain seat-filling events to guide direct adjustments of the seat protection level in accordance with the optimality conditions of Brumelle and McGill (1993).
Abstract: We investigate a simple adaptive approach to optimizing seat protection levels in airline revenue management systems. The approach uses only historical observations of the relative frequencies of certain seat-filling events to guide direct adjustments of the seat protection levels in accordance with the optimality conditions of Brumelle and McGill (1993). Stochastic approximation theory is used to prove the convergence of this adaptive algorithm to the optimal protection levels. In a simulation study, we compare the revenue performance of this adaptive approach to a more traditional method that combines a censored forecasting method with a common seat allocation heuristic (EMSR-b).
162 citations
TL;DR: It is demonstrated through theoretic analysis that in the presence of undernulled interference, the ASB is a pliable false alarm regulatory (FAR) detector that maintains good target sensitivity.
Abstract: A two-dimensional (2-D) adaptive sidelobe blanker (ASB) detection algorithm was developed through experimentation as an extenuate for false alarms caused by undernulled interference encountered when applying the adaptive matched filter (AMF) in nonhomogeneous environments. The algorithm's utility has been demonstrated empirically. Considering theoretic performance analyses of the ASB detection algorithm as well as the AMF generalized likelihood ratio test (GLRT), and the adaptive cosine estimator (ACE), under nonideal conditions, can become fairly intractable rather quickly, especially in an adaptive processing context involving covariance estimation. In this paper, however, we have developed and exploited a theoretic framework through which the performance of these algorithms under nonhomogeneous conditions can be examined theoretically. It is demonstrated through theoretic analysis that in the presence of undernulled interference, the ASB is a pliable false alarm regulatory (FAR) detector that maintains good target sensitivity. A viable method of ASB threshold selection is also presented and demonstrated.
162 citations
01 Nov 2000
TL;DR: In this paper, an adaptive sliding-mode control system is proposed to control the position of an induction servomotor drive, which is insensitive to uncertainties including parameter variations and external disturbance in the whole control process.
Abstract: An adaptive sliding-mode control system is proposed to control the position of an induction servomotor drive. First, a newly designed total sliding-mode control system, which is insensitive to uncertainties including parameter variations and external disturbance in the whole control process, is introduced. The total sliding-mode control comprises the baseline model design and the curbing controller design. In the baseline model design a computed torque controller is designed to cancel the nonlinearity of the nominal plant. In the curbing controller design an additional controller is designed using a new sliding surface to ensure the sliding motion through the entire state trajectory. Therefore, in the total sliding-mode control system the controlled system has a total sliding motion without a reaching phase. Then, to relax the requirement for the bound of uncertainties, an adaptive sliding-mode control system is investigated to control the induction servomotor. In the adaptive sliding-mode control system a simple adaptive algorithm is utilised to estimate the bound of uncertainties. Simulated and experimental results due to periodic sinusoidal command show that the dynamic behaviours of the proposed control systems are robust with regard to uncertainties.
TL;DR: This work proposes a simple stochastic adaptive algorithm that can provide a substantial reduction in BER with no increase in complexity and is compared to the least-mean-square algorithm.
Abstract: We consider the design and adaptation of a linear equalizer with a finite number of coefficients in the context of a classical linear intersymbol-interference channel with Gaussian noise and a memoryless decision device. If the number of equalizer coefficients is sufficient, the popular minimum mean-squared-error (MMSE) linear equalizer closely approximates the optimal linear equalizer that directly minimizes bit-error rate (BER). However, when the number of equalizer coefficients is insufficient to approximate the channel inverse, the minimum-BER equalizer can outperform the MMSE equalizer by as much as 16 dB in certain cases. We propose a simple stochastic adaptive algorithm for realizing the minimum-BER equalizer. Compared to the least-mean-square algorithm, the proposed algorithm can provide a substantial reduction in BER with no increase in complexity.
TL;DR: Two approaches to the implementation of the conjugate gradient algorithm for filtering where several modifications to the original CG method are proposed are presented and it is shown that in finite word-length computation and close to steady state, the algorithms' behaviors are similar to the steepest descent algorithm.
Abstract: The paper presents and analyzes two approaches to the implementation of the conjugate gradient (CG) algorithm for filtering where several modifications to the original CG method are proposed. The convergence rates and misadjustments for the two approaches are compared. An analysis in the z-domain is used in order to find the asymptotic performance, and stability bounds are established. The behavior of the algorithms in finite word-length computation are described, and dynamic range considerations are discussed. It is shown that in finite word-length computation and close to steady state, the algorithms' behaviors are similar to the steepest descent algorithm, where the stalling phenomenon is observed. Using 16-bit fixed-point number representation, our simulations show that the algorithms are numerically stable.
TL;DR: An approach to parallelization of structured adaptive mesh refinement algorithms based on a message-passing model that exploits the coarse-grained parallelism inherent in the algorithms to achieve sufficient resolution in the solution.
Abstract: We describe an approach to parallelization of structured adaptive mesh refinement algorithms This type of adaptive methodology is based on the use of local grids superimposed on a coarse grid to achieve sufficient resolution in the solution The key elements of the approach to parallelization are a dynamic load-balancing technique to distribute work to processors and a software methodology for managing data distribution and communications The methodology is based on a message-passing model that exploits the coarse-grained parallelism inherent in the algorithms The approach is illustrated for an adaptive algorithm for hyperbolic systems of conservation laws in three space dimensions A numerical example computing the interaction of a shock with a helium bubble is presented We give timings to illustrate the performance of the method
TL;DR: It is demonstrated that under homogeneous data conditions with no signal array response mismatch that the adaptive sidelobe blanker algorithm is a constant false alarm rate (CFAR) algorithm and has an overall performance that is commensurate with Kelly's (1986) benchmark generalized likelihood ratio test (GLRT).
Abstract: The adaptive sidelobe blanker (ASB) algorithm is a two-stage detector consisting of a first stage adaptive matched filter (AMF) detector followed by a second-stage detector called the adaptive coherence (or cosine) estimator (ACE). Only those data test cells that survive both detection thresholdings are declared signal (target) bearing. We provide exact novel closed-form expressions for the resulting probability of detection (PD) and false alarm (PFA) for the ASB algorithm and demonstrate that under homogeneous data conditions with no signal array response mismatch that (i) the ASB is a constant false alarm rate (CFAR) algorithm, (ii) the ASB has a higher or commensurate PD for a given PFA than both the AMF and the ACE, and (iii) the ASB has an overall performance that is commensurate with Kelly's (1986) benchmark generalized likelihood ratio test (GLRT). A compact statistical summary is derived providing distributions and dependencies among the GLRT, AMF, and the ACE decision statistics.
TL;DR: A perceptual-based image coder, which discriminates between image components based on their perceptual relevance for achieving increased performance in terms of quality and bit rate, which is based on a locally adaptive perceptual quantization scheme for compressing the visual data.
Abstract: Most existing efforts in image and video compression have focused on developing methods to minimize not perceptual but rather mathematically tractable, easy to measure, distortion metrics. While nonperceptual distortion measures were found to be reasonably reliable for higher bit rates (high-quality applications), they do not correlate well with the perceived quality at lower bit rates and they fail to guarantee preservation of important perceptual qualities in the reconstructed images despite the potential for a good signal-to-noise ratio (SNR). This paper presents a perceptual-based image coder, which discriminates between image components based on their perceptual relevance for achieving increased performance in terms of quality and bit rate. The new coder is based on a locally adaptive perceptual quantization scheme for compressing the visual data. Our strategy is to exploit human visual masking properties by deriving visual masking thresholds in a locally adaptive fashion based on a subband decomposition. The derived masking thresholds are used in controlling the quantization stage by adapting the quantizer reconstruction levels to the local amount of masking present at the level of each subband transform coefficient. Compared to the existing non-locally adaptive perceptual quantization methods, the new locally adaptive algorithm exhibits superior performance and does not require additional side information. This is accomplished by estimating the amount of available masking from the already quantized data and linear prediction of the coefficient under consideration. By virtue of the local adaptation, the proposed quantization scheme is able to remove a large amount of perceptually redundant information. Since the algorithm does not require additional side information, it yields a low entropy representation of the image and is well suited for perceptually lossless image compression.
TL;DR: In this paper, an adaptive genetic approach is proposed as an effective means of providing the optimal solution to the manufacturing cell formation problem in the design of cellular manufacturing systems, which generates the optimal formation of machine cells and part families by sequencing the rows and columns of a machine-part incidence matrix, so as to maximise the bond energy of the incidence matrix.
Abstract: An adaptive genetic approach is proposed as an effective means of providing the optimal solution to the manufacturing cell formation problem in the design of cellular manufacturing systems. The proposed approach generates the optimal formation of machine cells and part families by sequencing the rows and columns of a machine-part incidence matrix, so as to maximise the bond energy of the incidence matrix. In order to enhance the performance of the genetic search process, an adaptive scheme is adopted, so that the genetic parameters can be adjusted during the genetic search process. The effectiveness of the proposed approach is demonstrated by applying it to two numerical examples and 11 benchmark problems obtained from the literature. The computational results show that the proposed approach provides a powerful but simple means of solving the manufacturing cell formation problem and thus facilitates the design of cellular manufacturing systems.
TL;DR: The proposed beamforming algorithm is applied to the base station of a code-division-multiple access (CDMA) mobile communication system and the performance is shown in multipath fading communication channels in terms of the signal-to-interference+noise ratio, the bit error rate, and the achievable capacity of a given CDMA cell/sector.
Abstract: An alternative way of adaptive beamforming is presented. The main contribution of the new technique is in its simplicity with a minimal loss of accuracy. The total computational load for computing a suboptimal weight vector from each new signal vector is about O(2N/sup 2/+5N). It can further be reduced down to O(3N) by approximating the autocorrelation matrix with the instantaneous signal vector at each snapshot. The required condition on the adaptive gain for the proposed algorithm to converge is derived analytically. The proposed beamforming algorithm is applied to the base station of a code-division-multiple access (CDMA) mobile communication system. The performance of the proposed method is shown in multipath fading communication channels in terms of the signal-to-interference+noise ratio (SINR), the bit error rate (BER), and the achievable capacity of a given CDMA cell/sector.
TL;DR: Adaptive fuzzy-based tracking control designs are proposed in this article for both holonomic mechanical systems as well as a large class of nonholonomic mechanical system with plant uncertainties and external disturbances.
Abstract: Adaptive fuzzy-based tracking control designs are proposed in the paper for both holonomic mechanical systems as well as a large class of nonholonomic mechanical systems with plant uncertainties and external disturbances. A unified and systematic procedure is employed to derive the controllers for both holonomic and nonholonomic mechanical control systems, respectively. First, a fuzzy logic system is introduced to learn the behavior of unknown (or uncertain) mechanical dynamics by using an adaptive algorithm. Next, the effect of approximation error on the tracking error must be efficiently eliminated by employing an additional robustifying algorithm. Consequently, hybrid adaptive-robust controllers can be constructed such that the resulting closed-loop mechanical systems guarantee a satisfactorily transient and asymptotic performance. Furthermore, a partitioned procedure with respect to the above developed adaptive fuzzy logic approximators is introduced such that the number of fuzzy IF-THEN rules is significantly reduced and the developed control schemes can be easily implemented from the viewpoint of practical applications. Finally, simulation examples are presented to illustrate the tracking performance of a two-link constrained robot manipulator and a vertical wheel rolling on a plane surface by the proposed adaptive fuzzy-based control algorithms.
TL;DR: This approach addresses the load balancing problem in a new way, requiring far less communication than current approaches, and allows existing sequential adaptive PDE codes such as PLTMG and MC to run in a parallel environment without a large investment in recoding.
Abstract: We present a new approach to the use of parallel computers with adaptive finite element methods. This approach addresses the load balancing problem in a new way, requiring far less communication than current approaches. It also allows existing sequential adaptive PDE codes such as PLTMG and MC to run in a parallel environment without a large investment in recoding. In this new approach, the load balancing problem is reduced to the numerical solution of a small elliptic problem on a single processor, using a sequential adaptive solver, without requiring any modifications to the sequential solver. The small elliptic problem is used to produce a posteriori error estimates to predict future element densities in the mesh, which are then used in a weighted recursive spectral bisection of the initial mesh. The bulk of the calculation then takes place independently on each processor, with no communication, using possibly the same sequential adaptive solver. Each processor adapts its region of the mesh independently, and a nearly load-balanced mesh distribution is usually obtained as a result of the initial weighted spectral bisection. Only the initial fan-out of the mesh decomposition to the processors requires communication. Two additional steps requiring boundary exchange communication may be employed after the individual processors reach an adapted solution, namely, the construction of a global conforming mesh from the independent subproblems, followed by a final smoothing phase using the subdomain solutions as an initial guess. We present a series of convincing numerical experiments which illustrate the effectiveness of this approach. The justification of the initial refinement prediction step, as well as the justification of skipping the two communication-intensive steps, is supported by some recent [J. Xu and A. Zhou, Math. Comp., to appear] and not so recent [J. A. Nitsche and A. H. Schatz, Math. Comp., 28 (1974), pp. 937--958; A. H. Schatz and L. B. Wahlbin, Math. Comp., 31 (1977), pp. 414--442; A. H. Schatz and L. B. Wahlbin, Math. Comp., 64 (1995), pp. 907--928] results on local a priori and a posteriori error estimation.
TL;DR: An adaptive model-driven bit-allocation algorithm for video sequence coding based on a parametric rate-distortion model, which exploits characteristics of human visual perception to efficiently allocate bits according to a region's visual importance.
Abstract: We present an adaptive model-driven bit-allocation algorithm for video sequence coding. The algorithm is based on a parametric rate-distortion model, and facilitates both picture-and macroblock-level bit allocation. A region classification scheme is incorporated into the algorithm, which exploits characteristics of human visual perception to efficiently allocate bits according to a region's visual importance. The application of this algorithm to MPEG video coding is discussed in detail. We show that the proposed algorithm is computationally efficient and has many advantages over the MPEG-2 TM5 bit-allocation algorithm.
TL;DR: A new, efficient stereo algorithm addressing robust disparity estimation in the presence of occlusions is presented, an adaptive, multiwindow scheme using left–right consistency to compute disparity and its associated uncertainty.
Abstract: We present a new, efficient stereo algorithm addressing robust disparity estimation in the presence of occlusions. The algorithm is an adaptive, multiwindow scheme using left–right consistency to compute disparity and its associated uncertainty. We demonstrate and discuss performances with both synthetic and real stereo pairs, and show how our results improve on those of closely related techniques for both accuracy and efficiency.
TL;DR: A new stochastic analysis is presented for the filtered-X LMS (FXLMS) algorithm and an analytical model is derived for the mean behavior of the adaptive weights.
Abstract: A new stochastic analysis is presented for the filtered-X LMS (FXLMS) algorithm. The analysis does not use independence theory. An analytical model is derived for the mean behavior of the adaptive weights. The model is valid for white or colored reference inputs and accurately predicts the mean weight behavior even for large step sizes. The constrained Wiener solution is determined as a function of the input statistics and the impulse responses of the adaptation loop filters. Effects of secondary path estimation error are studied. Monte Carlo simulations demonstrate the accuracy of the theoretical model.
TL;DR: In CDMA systems with long codes, the users' signatures change in each bit period, impeding the estimation of the time-invariant multipath parameters, so correlation-matching methods are employed to blindly estimate those multipATH parameters.
Abstract: In CDMA systems with long codes, the users' signatures change in each bit period, impeding the estimation of the time-invariant multipath parameters. In this paper, correlation-matching methods are employed to blindly estimate those multipath parameters. For given code sequences, the output correlation matrix (parameterized by the unknown channel coefficients) is compared with its instantaneous approximation. By minimizing the Frobenius norm of the resulting error matrix, the channel parameters can be estimated up to a complex scalar ambiguity. Both batch and adaptive algorithms are derived. Under the assumption of i.i.d. code sequences, identifiability up to a complex scalar ambiguity for each channel is guaranteed, and the asymptotic convergence of the proposed algorithm is established. Furthermore, step-size selection for the adaptive version is investigated. When only the code sequence of the user of interest is available, a single user receiver is also derived. Simulation results verify those claims and provide comparisons with other methods.
TL;DR: The results indicate that, within the parameter sub-space where its rules are trained, the fuzzy rule-based model provided solutions with low mean square error between observations and predictions.
Abstract: This paper describes a fuzzy rule-based approach applied for reconstruction of missing precipitation events. The working rules are formulated from a set of past observations using an adaptive algorithm. A case study is carried out using the data from three precipitation stations in northern Italy. The study evaluates the performance of this approach compared with an artificial neural network and a traditional statistical approach. The results indicate that, within the parameter sub-space where its rules are trained, the fuzzy rule-based model provided solutions with low mean square error between observations and predictions. The problems that have yet to be addressed are overfitting and applicability outside the range of training data.
23 Aug 2000
TL;DR: A unified mathematical treatment of most adaptive matched filter detectors using common notation is presented, and the underlying theoretical assumptions are state clearly, to identify best-of-class algorithms for detailed performance evaluation.
Abstract: Real-time detection and identification of military and civilian targets from airborne platforms using hyperspectral sensors is of great interest. Relative to multispectral sensing, hyperspectral sensing can increase the detectability of pixel and subpixel size targets by exploiting finer detail in the spectral signatures of targets and natural backgrounds. A multitude of adaptive detection algorithms for resolved or subpixel targets, with known or unknown spectral characterization, in a background with known or unknown statistics, theoretically justified or ad hoc, with low or high computational complexity, have appeared in the literature or have found their way into software packages and end-user systems. The purpose of this paper is threefold. First, we present a unified mathematical treatment of most adaptive matched filter detectors using common notation, and we state clearly the underlying theoretical assumptions. Whenever possible, we express existing ad hoc algorithms as computationally simpler versions of optimal methods. Second, we assess the computational complexity of the various algorithms. Finally, we present a comparative performance analysis of the basic algorithms using theoretically obtained performance characteristics. We focus on algorithms characterized by theoretically desirable properties, practically desired features, or implementation simplicity. Sufficient detail is provided for others to verify and expand this evaluation and framework. A primary goal is to identify best-of-class algorithms for detailed performance evaluation.
TL;DR: In this article, the R/sub 2/ estimation in the transient state without signal injection to the stator current is proposed, which uses the least mean square algorithm and the adaptive algorithm.
Abstract: In the speed sensorless control of the induction motor, the machine parameters (especially rotor resistance R/sub 2/) have a strong influence on the speed estimation. It is known that the simultaneous estimation of the rotor speed and R/sub 2/ is impossible in the slip frequency type vector control, because the rotor flux is constant. But the rotor flux is not always constant in the speed transient state. In this paper, the R/sub 2/ estimation in the transient state without signal injection to the stator current is proposed. This algorithm uses the least mean square algorithm and the adaptive algorithm, and it is possible to estimate R/sub 2/ exactly. This algorithm is verified by the digital simulations and experiments.
01 Feb 2000
TL;DR: Analogue adaptive filters represent an important niche in adaptive-filter theory and practice and their important adaptive algorithms, filter structures, circuit techniques and applications are discussed.
Abstract: Analogue adaptive filters represent an important niche in adaptive-filter theory and practice. The paper provides an overview of the field. Important adaptive algorithms, filter structures, circuit techniques and applications are all discussed. Considerable attention is paid to historically significant developments which have provided a foundation for modern analogue adaptive filters. Future directions are also surmised.
TL;DR: A very simple neural network model and an efficient on-line adaptive algorithm that sequentially extract temporally correlated sources with arbitrary distributions, including colored Gaussian sources and sources with extremely low values (or even zero) of kurtosis are developed.
Abstract: Most of the algorithms for blind separation/extraction and independent component analysis (ICA) can not separate mixtures of sources with extremely low kurtosis or colored Gaussian sources. Moreover, to separate mixtures of super- and sub-Gaussian signals, it is necessary to use adaptive (time-variable) or switching nonlinearities which are controlled via computationally intensive measures, such as estimation of the sign of kurtosis of extracted signals. In this paper, we develop a very simple neural network model and an efficient on-line adaptive algorithm that sequentially extract temporally correlated sources with arbitrary distributions, including colored Gaussian sources and sources with extremely low values (or even zero) of kurtosis. The validity and performance of the algorithm have been confirmed by extensive computer simulation experiments.
TL;DR: An adaptive block based intra refresh algorithm for increasing error robustness in an interframe coding system is described, demonstrating a significant improvement in terms of error recovery time over nonadaptive intra update strategies.
Abstract: An adaptive block based intra refresh algorithm for increasing error robustness in an interframe coding system is described. The goal of this algorithm is to allow the intra update rates for different image regions to vary according to various channel conditions and image characteristics. The update scheme is based on an "error-sensitivity metric," accumulated at the encoder, representing the vulnerability of each coded block to channel errors. As each new frame is encoded, the accumulated metric for each block is examined, and those blocks deemed to have an unacceptably high metric are sent using intra coding as opposed to inter coding. This approach requires no feedback channel and is fully compatible with H.263. It involves a negligible increase in encoder complexity and no change in the decoder complexity. Simulations performed using an H.263 bitstream corrupted by channel errors demonstrate a significant improvement in terms of error recovery time over nonadaptive intra update strategies.