Showing papers on "Adaptive algorithm published in 1970"

Adaptive gain control

Abstract: Communications and radar receivers of advanced design may utilize real time computers for signal processing. In such instances an adaptive learning scheme can be employed to optimize use of the receiver's dynamic range. An adaptive algorithm for statistically optimum AGC is derived in which the receiver gain is adjusted during the learning process so that the average cost of excluding the received signal from the receiver's dynamic range is minimized. Application of the approach to the frequently occurring situation of Rayleigh distributed signals of unknown strength yields a recursive algorithm which can be easily implemented.

Analysis of Low Complexity Adaptive Step-size Orthogonal Gradient-based FEQ for OFDM Systems

Abstract: We propose two low complexity adaptive step-size mechanisms based on the normalised orthogonal gradient algorithm for frequency-domain equalisation in orthogonal frequency division multiplexing (OFDM) systems. These algorithms are derived from employing a mixed-subcarrier exponentially weighted least squares criterion. Two low complexity adaptive stepsize approaches are investigated by exploiting an estimate of autocorrelation between previous and present weight-estimated mixed-subcarrier errors. We compare our approaches with a previously fixed stepsize normalised orthogonal gradient adaptive algorithm and other existing algorithm for implementation. Simulation results demonstrate that the proposed algorithms can achieve good performance for involving an OFDM receiver.

Use Of Local Reanalysis And QuadraticH-hierarchical Functions In AdaptiveBoundary Element Models

Abstract: An adaptive boundary element scheme is developed using the concept of local reanalysis and quadratic h-hierarchical functions for the construction of near optimal computational models. The use of local reanalysis in the error estimation guarantees the reliability of the modeling process while the use of h-hierarchical elements guarantees the efficiency of the adaptive algorithm. The technique is developed for the elastic analysis of two-dimensional models. A practical example of a micro structure component shows the rapid convergence of the results with few refinement steps. INTRODUCTION Adaptivity is the availability of a reliable error analysis allowing the discretized model to be selectively refined and thus improved. In more precise terms Rank [1] states that the key to adaptivity is " ••• a more or less accurate knowledge about the size and distribution of the error for a given FEM or BIEM approximation. Usually this knowledge has to be extracted a posteriori from the numerical solution in the form of error indicators for the local error and error estimator for the global error in some norm ". The refinement will be necessary when the solution obtained from the analysis is not satisfactory. The accuracy of the solution may be verified by use of error estimators, but engineering considerations may also be used to test the reliability of the results. Once it has been judged necessary to refine, error indicators are used to show where the refinement should occur. Transactions on Modelling and Simulation vol 3, © 1993 WIT Press, www.witpress.com, ISSN 1743-355X

Adaptive Forgetting-factor RLS-based Initialisation Per-tone Equalisation in Discrete Multitone Systems

Abstract: An adaptive forgetting-factor inverse square-root recursive least squares (AF-iQRRLS) with inverse of correlation matrix updating is presented for per-tone equalisation in discrete multitone-based systems. The proposed inverse covariance update of the square-root covariance Kalman filter is introduced to prepare for the signal flow graph (SFG). This reduced derivation of adaptive inverse square-root recursive least squares algorithm can modify via SFG. In order to reduce the computational complexity, the forgetting-factor parameter for each group called per-group forgettingfactor (PGFF) approach based on AF-iQRRLS algorithm is introduced. The forgetting-factor from the middle of each group is selected as a representative in order to find an optimal forgetting-factor parameter by using AF-iQRRLS algorithm. After convergence, it is fixed for remaining tones of whole group. Simulation results reveal that the trajectories of modified PGFF of the proposed algorithm for each individual tone can converge to their own equilibria. Moreover, the performance of the proposed algorithms are improved as compared with the existing algorithm.

Frequency-Domain Maximum-Likelihood Adaptive Filtering

Abstract: : Recent intensive study of adaptive (gradient-search) filtering in the time domain has not solved the problems with rate-of-convergence problem, which is a major difficulty with this technique. A recent study based on a set of time-stationary synthetic data shows that the time-domain maximum-likelihood adaptive filter converges very slowly to the optimum filter. After 3300 iterations of adaption with an adaptive rate of 10 percent of maximum value, the adaptive filter is still about 4 db away from the optimum filter in the sense of mean-square outputs. Time-domain adaptive filtering necessitates using only one convergence parameter for all filter coefficients, which may cause slow convergence for some data. Frequency-domain adaptive filtering may solve this problem, since different convergence parameters can be used for different frequency components. This report describes a frequency-domain maximum- likelihood adaptive-filtering algorithm analogous to the time-domain adaptive algorithm. This algorithm was used with a set of synthetic stationary data previously used for a time-domain adaptive-filtering study. Different filter lengths and convergence parameters were used. Results are compared with beamsteer and time-domain adaptive filter.

Convergence of certain training algorithms for adaptive threshold elements with imperfect weight components

Abstract: A proof of the convergence of an adaptive algorithm based on the concept of characteristic vector is presented. It is shown that the adaptive algorithm will converge even when imperfections existing in the components of the ATE and the imperfections are within certain finite limits.