In this paper we study and solve two different variants of static knapsack problems with random weights: The stochastic knapsack problem with simple recourse as well as the stochastic knapsack problem with probabilist...
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In this paper we study and solve two different variants of static knapsack problems with random weights: The stochastic knapsack problem with simple recourse as well as the stochastic knapsack problem with probabilistic constraint. Special interest is given to the corresponding continuous problems and three different problem solving methods are presented. The resolution of the continuous problems allows to provide upper bounds in a branch-and-bound framework in order to solve the original problems. Numerical results on a dataset from the literature as well as a set of randomly generated instances are given.
In this paper, we propose a practical and systematic approach to implement the MIMO transmission with rank adaptation for 60 GHz systems. In the 60 GHz system with multiple antennas, the transmit and receive (Tx-Rx) a...
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ISBN:
(纸本)9781424456383
In this paper, we propose a practical and systematic approach to implement the MIMO transmission with rank adaptation for 60 GHz systems. In the 60 GHz system with multiple antennas, the transmit and receive (Tx-Rx) antenna arrays are grouped into a number of subarrays with a predetermined subarray separation based on the derived geometrical criteria of creating high rank MIMO in LoS environments. We first apply an enhanced blind beamforming technique based on a stochastic gradient algorithm (SGA) for the inner-subarray antennas, which does not require channel state information (CSI) at either the transmitter or the receiver. Secondly, the composite MIMO channel, as a joint effect of Tx-Rx beamforming and the channel impulse response, can be estimated with much reduced complexity. Finally, the MIMO transmission with rank adaptation is performed by adaptively selecting the better scheme out of the high-rank spatial multiplexing and the rank-1 beamforming whichever gives a higher system throughput. Simulation results show that high-rank spatial multiplexing and rank-1 beamforming outperform each other at different geometrical placements and transmit power settings. The proposed MIMO transmission with rank adaptation offers significant performance gain especially at high signal-to-noise ratio (SNR) regions.
This paper studies the parameter identification problem of Hammerstein output-error systems with two-segment nonlinearities. New switching sequences are proposed to reduce the number of parameters to be estimated. A s...
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This paper studies the parameter identification problem of Hammerstein output-error systems with two-segment nonlinearities. New switching sequences are proposed to reduce the number of parameters to be estimated. A stochasticgradient identification algorithm together with the convergence analysis is further developed. A varying forgetting factor scheme is used to make a tradeoff between the convergence rate and estimation accuracy. Both numerical simulation results and applications to distillation columns are provided to show the effectiveness of the proposed method.
Traditional filtering theory is always based on optimization of the expected value of a suitably chosen function of error, Such as the minimum mean-square error (MMSE) criterion, the minimum error entropy (MEE) criter...
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Traditional filtering theory is always based on optimization of the expected value of a suitably chosen function of error, Such as the minimum mean-square error (MMSE) criterion, the minimum error entropy (MEE) criterion, and so oil. None of those criteria could capture all the probabilistic information about the error distribution. In this work, we propose a novel approach to shape the probability density function (PDF) of the errors in adaptive filtering. As the PDF contains all the probabilistic information, the proposed approach can be used to obtain the desired variance or entropy, and is expected to be useful in the complex signal processing and learning systems. In our method, the information divergence between the actual errors and the desired errors is chosen as the cost function, which is estimated by kernel approach. Some important properties of the estimated divergence are presented. Also, for the finite impulse response (FIR) Filter, a stochastic gradient algorithm is derived. Finally, simulation examples illustrate the effectiveness of this algorithm in adaptive system training.
Adaptive filtering has been an enabling technology and has found ever-increasing applications in various state-of-the-art communication systems. Traditionally, adaptive filtering has been developed based on the Wiener...
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Adaptive filtering has been an enabling technology and has found ever-increasing applications in various state-of-the-art communication systems. Traditionally, adaptive filtering has been developed based on the Wiener or minimum mean square error (MMSE) approach, and the famous least mean square algorithm with its low computational complexity readily meets the fast real-time computational constraint of modern high-speed communication systems. For a communication system, however, it is the system's bit error rate (BER), not the mean square error (MSE), that really matters. It has been recognised that minimising the MSE criterion does not necessarily produce the minimum BER (MBER) performance. The introduction of the novel MBER design has opened up a whole new chapter in the optimisation of communication systems, and its design trade-offs have to be documented in contrast to those of the classic but actually still unexhausted MMSE and other often-used optimisation criteria. This contribution continues this theme, and we provide a generic framework for adaptive minimum error-probability filter design suitable for the employment in a variety of communication systems. Advantages and disadvantages of the adaptive minimum error-probability filter design are analysed extensively, in comparison with the classic Wiener filter design. (c) 2008 Elsevier B.V. All rights reserved.
Conventional cost functions of adaptive filtering are usually related to the error's dispersion, such as error's moments or error's entropy, but neglect the shape aspects (peaks, kurtosis, tails, etc.) of ...
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ISBN:
(纸本)9781424418206
Conventional cost functions of adaptive filtering are usually related to the error's dispersion, such as error's moments or error's entropy, but neglect the shape aspects (peaks, kurtosis, tails, etc.) of the error distribution. In this work, we propose a new notion of filtering (or estimation) in which the error's probability density function (PDF) is shaped into a desired one. As PDFs contain all the probabilistic information, the proposed method can be used to achieve the desired error variance or error entropy, and is expected to be useful in the complex signal processing and learning systems. In our approach, the information divergence between the actual errors and the desired errors is used as the cost function. By kernel density estimation, we derive the associated stochastic gradient algorithm for the finite impulse response (FIR) filter. Simulation results emphasize the effectiveness of this new algorithm in adaptive system training.
The maximum mutual information (MaxMI) criterion is used as the adaptation cost for the adaptive filtering. This criterion is robust to measure distortions, and has strong connection with traditional mean-square error...
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The maximum mutual information (MaxMI) criterion is used as the adaptation cost for the adaptive filtering. This criterion is robust to measure distortions, and has strong connection with traditional mean-square error (MSE) criterion. Under Gaussian assumption, the closed-form solution of the finite impulse response (FIR) filter is obtained. Further, based on the kernel density estimation, the stochastic mutual information gradient (SMIG) algorithm is derived. Simulation results emphasize the robustness of this new algorithm. (C) 2008 Elsevier B.V. All rights reserved.
The autoregressive moving average exogenous （ARMAX） model is commonly adopted for describing linear stochastic systems driven by colored noise. The model is a finite mixture with the ARMA component and external inpu...
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The autoregressive moving average exogenous （ARMAX） model is commonly adopted for describing linear stochastic systems driven by colored noise. The model is a finite mixture with the ARMA component and external inputs. In this paper we focus on a parameter estimate of the ARMAX model. Classical modeling methods are usually based on the assumption that the driven noise in the moving average （MA） part has bounded variances, while in the model considered here the variances of noise may increase by a power of log n. The plant parameters are identified by the recursive stochastic gradient algorithm. The diminishing excitation technique and some results of martingale difference theory are adopted in order to prove the convergence of the identification. Finally, some simulations are given to show the reliability of the theoretical results.
The basic objective of blind signal separation is to recover a set of source signals from a set of observations that are mixtures of the sources with no, or very limited knowledge about the mixture structure and sourc...
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The basic objective of blind signal separation is to recover a set of source signals from a set of observations that are mixtures of the sources with no, or very limited knowledge about the mixture structure and source signals. To extract the original sources, many algorithms have been proposed;among them, the cross-correlation and constant modulus algorithm (CC-CMA) appears to be the algorithm of choice due to its computational simplicity. An important issue in CC-CMA algorithm is the global convergence analysis, because the cost function is not quadratic nor convex and contains undesirable stationary points. If these undesirable points are local minimums, the convergence of the algorithm may not be guaranteed and the CC-CMA would fail to separate source signals. The main result of this paper is to complete the classification of these stationary points and to prove that they are not local minimums unless if the mixing parameter is equal to 1. This is obtained by using the theory of discriminant varieties to determine the stationnary points as a function of the parameter and then to show that the Hessian matrix of the cost function is not positive semidefinite at these stationnay points, unless if the mixing parameter is 1.
An adaptive error-constrained least mean square (AECLMS) algorithm is derived and proposed using adaptive error-constrained optimization techniques. This is accomplished by modifying the cost function of the LMS algor...
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An adaptive error-constrained least mean square (AECLMS) algorithm is derived and proposed using adaptive error-constrained optimization techniques. This is accomplished by modifying the cost function of the LMS algorithm using augmented Lagrangian multipliers. Theoretical analyses of the proposed method are presented in detail. The method shows improved performance in terms of convergence speed and misadjustment. This proposed adaptive errorconstrained method can easily be applied to and combined with other LMS-type stochasticalgorithms. Therefore, we also apply the method to constant modulus criterion for blind method and backpropagation algorithm for multilayer perceptrons. Simulation results show that the proposed method can accelerate the convergence speed by 2 to 20 times depending on the complexity of the problem. (c) 2005 Elsevier B.V. All rights reserved.
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