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On Using Recursive Least Squares In SamplePath Optimization Of Discrete Event Systems
, 1995
"... This paper describes a simple algorithm that can be used to optimize a performance function of certain discrete event systems with respect to continuous decision variables. By simulating the underlying stochastic process using common random numbers, the resulting function is a deterministic function ..."
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Cited by 1 (0 self)
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function; and by taking successive approximation by a means of a quadratic function whose coefficients are estimated by means of the recursive least squares, the approximate function is then optimized using a deterministic optimization algorithm to obtain an estimate of a solution. The proposed algorithm
NONLINEAR PREDICTIVE ANALYSIS OF SPEECH BY ITERATIVE APPROACH
"... The filter involving the adaptation scheme of Volterra Series Least Mean Square(VSLMS) algorithm is a representative adaptive nonlinear filter, which has been applied to a lot of engineering applications. However, when the VSLMS filter is used as an adaptive predictor of speech, a large number of sp ..."
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of speech data samples are required to minimize the predictive error. And if the VSLMS predictor is used for shortterm prediction with a high order of the quadratic kernel to increase the predictive gain, it is suffered from its numerical unstability. To conquer such problems, an iterative approach
The Application of Bayesian Inference to Linear Prediction of Speech
, 1994
"... The analysis of a speech segment is conventionally performed through linear prediction and the subsequent minimisation of a data error term in the least squares sense. The parameters derived as such maximise the likelihood of the data. In a learning problem, the addition of penalty terms, or regular ..."
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The analysis of a speech segment is conventionally performed through linear prediction and the subsequent minimisation of a data error term in the least squares sense. The parameters derived as such maximise the likelihood of the data. In a learning problem, the addition of penalty terms
Adaptive Predictive Controllers Using a Growing and Pruning RBF Neural Network
"... ABSTRACT: An adaptive version of growing and pruning RBF neural network has been used to predict the system output and implement Linear ModelBased Predictive Controller (LMPC) and Nonlinear Modelbased Predictive Controller (NMPC) strategies. A radialbasis neural network with growing and pruning ..."
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improvement with faster response time for both servo and regulatory control objectives in comparison with the proposed adaptive LMPC, an adaptive generalized predictive controller based on Recursive Least Squares (RLS) algorithm and welltuned PID controllers.
Key words: BDU, LQR, Predictive Control, Robustness.
"... This work presents the BDU technique (Bounded Data Uncertainties) and the tuning of the linear quadratic regulator (LQR) via this technique, which considers models with bounded uncertainties. The BDU method is based on constrained gametype formulations, and allows the designer to explicitly incorpo ..."
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sensitive to errors design. A feature of this technique consists of its geometric interpretation. The structure of the paper is the following, in the first section some problems about the leastsquares method in the presence of uncertainty are introduced. The BDU technique is shown in the second section
1 MeanSquared Error Beamforming for Signal Estimation: A Competitive Approach
"... Beamforming is a classical method of processing temporal sensor array measurements for signal estimation, interference cancellation, source direction, and spectrum estimation. It has ubiquitously been applied in areas such as radar, sonar, wireless communications, speech processing, and medical im ..."
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Beamforming is a classical method of processing temporal sensor array measurements for signal estimation, interference cancellation, source direction, and spectrum estimation. It has ubiquitously been applied in areas such as radar, sonar, wireless communications, speech processing, and medical imaging (see, for example,
LMS2: Towards an algorithm that is as cheap as LMS and almost as efficient as RLS
"... Abstract — We consider linear prediction problems in a stochastic environment. The least mean square (LMS) algorithm is a wellknown, easy to implement and computationally cheap solution to this problem. However, as it is well known, the LMS algorithm, being a stochastic gradient descent rule, may c ..."
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converge slowly. The recursive least squares (RLS) algorithm overcomes this problem, but its computational cost is quadratic in the problem dimension. In this paper we propose a two timescale stochastic approximation algorithm which, as far as its slower timescale is considered, behaves the same way
SelfTuning Control of a Nonlinear Stochastic Systems Described by a Hammerstein Mathematical Model
"... In this paper, we developed the parametric estimation and the selftuning control problem of the nonlinear systems which are described by discretetime nonlinear mathematical models, with unknown, timevarying parameters, and operative in a stochastic environment. The parametric estimation is realiz ..."
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is realized by using the prediction error method and the recursive least squares techniques. The selftuning control problem is formulated by minimizing a certain quadratic criterion. An example of numerical simulation is treated in this paper, to test the proposed selftuning control method. General Terms
Documentation of JavaXCSF
, 2009
"... This report gives an overview of the JavaXCSF code and explains, where to get the code. Furthermore, the settings and features are described briefly. The document also explains how to use the code and how to extend it. JavaXCSF is an implementation of the XCSF Learning Classifier System that is used ..."
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Cited by 5 (2 self)
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that is used for function approximation. The code contains four types of conditions, namely rectangular, ellipsoidal, rotating rectangular, and rotating ellipsoidal conditions. In addition to a simple constant predictor based on the WidrowHoff rule, implementations of a linear recursive least squares (RLS
Online Adaptive Utilization Control for RealTime Embedded Multiprocessor Systems
"... To provide Quality of Service (QoS) guarantees in open and unpredictable environments, the utilization control problem is defined to keep the processor utilization at the schedulable utilization bound, even in the face of unpredictable and/or varying task execution times. To handle the endtoend ta ..."
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Cited by 13 (4 self)
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estimation errors, the system may suffer performance deterioration or even become unstable if the actual task execution times are much larger than their estimated values. In this paper, we present an online adaptive optimal control approach using Recursive Least Squares (RLS) based model estimator plus
Results 1  10
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