The blind equalizers based on complex valued feedforward neural networks, for linear and nonlinear communication channels, yield better performance as compared to linear equalizers. The learning algorithms are, genera...
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The blind equalizers based on complex valued feedforward neural networks, for linear and nonlinear communication channels, yield better performance as compared to linear equalizers. The learning algorithms are, generally, based on stochastic gradient descent, as they are simple to implement. However, these algorithms show a slow convergence rate. In the blind equalization problem, the unavailability of the desired output signal and the presence of nonlinear activation functions make the application of recursiveleastsquares algorithm difficult. In this letter, a new scheme using recursiveleastsquares algorithm is proposed for blind equalization. The learning of weights of the output layer is obtained by using a modified version of constant modulus algorithm cost function. For the learning of weights of hidden layer neuron space adaptation approach is used. The proposed scheme results in faster convergence of the equalizer.
In my research, the performance of multilayered perceptron (MLP) network which trained by recursiveleastsquare (RLS) algorithm is investigated. The network has been implemented to classify the cervical cells into no...
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Adaptive predistortion is one of the most promising techniques to overcome the nonlinear of High Power Amplifier (HPA). However, the adaptation of predistorter is not satisfied, for many efficient least-square (LS) ad...
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ISBN:
(纸本)0780376633
Adaptive predistortion is one of the most promising techniques to overcome the nonlinear of High Power Amplifier (HPA). However, the adaptation of predistorter is not satisfied, for many efficient least-square (LS) adaptive algorithm cannot be used. In this paper, we analyze previously published predistortion structures and their difficulties in adapting with these efficient algorithms. The proposed structure constructs the desired output of the predistorter, so many efficient LS adaptive algorithms can be used directly, which implies that we can obtain faster convergence and lower complexity in adaptive process. Simulation results using recursiveleastsquare (RLS) algorithm in adaptation demonstrate about 30dB spectrum spreading improvement.
In this paper, a method of updating filter coefficients is proposed in which the RLS (recursiveleastsquares) algorithm is applied to a noise canceler with a filter for crosstalk removal. The RLS algorithm has an adv...
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In this paper, a method of updating filter coefficients is proposed in which the RLS (recursiveleastsquares) algorithm is applied to a noise canceler with a filter for crosstalk removal. The RLS algorithm has an advantage over the least Mean squarealgorithm by virtue of better convergence. When the filter coefficients are updated including the filter for crosstalk removal, the error signal becomes nonlinear in terms of filter coefficients. Hence, a nonlinear RLS algorithm is derived in which such non-linearity is taken into account. It is shown that the convergence is further improved in comparison with the conventional RLS algorithm. Finally, a method is proposed to reduce the noise contained in the transmitted voice signals of telephone-by means of the present noise canceler and its effectiveness is evaluated. (C) 2003 Wiley Periodicals, Inc.
The authors present a new model for the ageing of a lead-acid battery which is based on the initial model of Shepherd. The proposed model allows to predict temporal variations of the Shepherd coefficients and to contr...
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The authors present a new model for the ageing of a lead-acid battery which is based on the initial model of Shepherd. The proposed model allows to predict temporal variations of the Shepherd coefficients and to control the deterioration of the battery parameters and performances. The model validation has been realised by the recursiveleastsquare (RLS) algorithm by using long-term measurements under several solicitations. This study will improve the storage section of stand-alone photovoltaic systems and reduce overloads and deep discharges. (C) 2002 Elsevier Science B.V. All rights reserved.
In this paper, it is framed a model of RBF neural network (RBFNN) to solve identification of nonlinear systems. First, it is proposed a kind of optimal selection cluster algorithm. By this algorithm, it is optimally g...
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ISBN:
(纸本)0780372689
In this paper, it is framed a model of RBF neural network (RBFNN) to solve identification of nonlinear systems. First, it is proposed a kind of optimal selection cluster algorithm. By this algorithm, it is optimally gained the hidden layer node number of RBFNN in terms of input samples. At the same time, it is obtained the initial parameters values of RBF. Then, it is estimated the parameters value of RBF by gradient algorithm with momentum terms, and identified the weights of RBFNN by recursive least square algorithm. With the above two algorithms, it is alternately iterated. By the above hybrid algorithms, it is not only raised identification precision of RBFNN, but also improved generalization property of the net. It is proved the validity of the scheme by its applications.
Array processing involves manipulation of signals induced on various antenna elements. Its capabilities of steering nulls to reduce cochannel interferences and pointing independent beams toward various mobiles, as wel...
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Array processing involves manipulation of signals induced on various antenna elements. Its capabilities of steering nulls to reduce cochannel interferences and pointing independent beams toward various mobiles, as well as its ability to provide estimates of directions of radiating sources, make it attractive to a mobile communications system designer. Array processing is expected to play an important role in fulfilling the increased demands of various mobile communications services. Part I of this paper showed how an array could be utilized in different configurations to improve the performance of mobile communications systems, with references to various studies where feasibility of an array system for mobile communications is considered. This paper provides a comprehensive and detailed treatment of different beam-forming schemes, adaptive algorithms tl? adjust the required weighting on antennas, direction-of-arrival estimation methods-including their performance comparison-and effects of errors on the performance of an array system, as well as schemes to alleviate them. This paper brings together almost all aspects of array signal processing. It is presented at a level appropriate to nonexperts in the field and contains a large reference list to probe further.
Chaotic modulation has recently been proposed for spread spectrum (SS) and code division multiple access (CDMA) communication. It embeds the signal of transmission in the bifurcating parameter of a chaotic dynamical s...
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Chaotic modulation has recently been proposed for spread spectrum (SS) and code division multiple access (CDMA) communication. It embeds the signal of transmission in the bifurcating parameter of a chaotic dynamical system, and uses the wide-band output of the chaotic system as the transmitted signal. In this brief, me consider the demodulation of this communication scheme using an adaptive filter. Not only can an adaptive filter reduce the effect of channel noise, but it also estimates the bifurcating parameter (i.e., the signal of transmission) sequentially which is required in a communication system, Three kinds of adaptive filters: the least mean square (LMS) algorithm, the recursiveleastsquare (RLS) technique and the Kalman filter, are considered here. It is found that the demodulators based on these adaptive filters outperform the standard inversion approach used in the literature.
One of the most important problems in signal processing is to estimate the output for a known input, here referred to as a query from the input/output (I/O) data seen to this point. A nonlinear adaptive estimation met...
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One of the most important problems in signal processing is to estimate the output for a known input, here referred to as a query from the input/output (I/O) data seen to this point. A nonlinear adaptive estimation method that uses a k-d trie (k-dimensional binary digital search tree) as an alternative to the k-d tree is presented here. The observed data are stored in the k-d trie, and a local model to answer each query is built by using the n nearest neighbors of the query. The database contents are updated as each new data point is available such that the latest N data are contained. This enables nonlinear adaptive signal processing in nonstationary environments. A storage procedure for the k-d trie that allows efficient update is presented for further reduction in computation time and storage requirement, A recursive least square algorithm, used for a recursive production of the local model, improves computational efficiency, Finally, a method of removing outliers is presented for improving estimation accuracy.
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