Showing papers on "Adaptive algorithm published in 1983"

Adaptive Control of Blood Pressure

Abstract: Stochastic adaptive controllers have been developed for automatic control of blood pressure during infusions of cardiostimulatory or vasoactive drugs. An adaptive algorithm based upon a minimum variance control law is presented. A more advanced algorithm obtained by augmenting the performance measure to include the rate of charge of the control signal is also presented. An autoregressive-moving-average (ARMA) model, representing the dynamics of the system, and a recursive least-squares parameter estimation technique are used for both algorithms. A series of experiments was performed in dogs, utilizing an electronically activated drug infuser. Stable control was achieved, even when the circulatory state of the animal underwent major changes, using either algorithm. On the basis of theoretical considerations and experimental results, we expect that these adaptive controllers will significantly improve the performance of drug infusion systems in clinical applications.

Convergence behavior of the constant modulus algorithm

Abstract: An adaptive filter algorithm has been developed and introduced [1] for use with constant envelope waveforms, e.g., FM communication signals. It has proven capable of suppressing additive interferers as well as equalization, without the need for a priori statistical information. In this paper, aspects of dynamic convergence behavior are discussed, with conclusions supported by simulation.

A finite-volume, adaptive grid algorithm applied to planetary entry flowfields

Abstract: An adaptive grid, finite volume method has been applied to problems of planetary entry in computing complete flowfields. The adaptation algorithm is implicit in nature and is keyed to resolve user-specified gradients. The finite-volume algorithm is explicit, utilizing a maximum time step advancement at each grid point to accelerate convergence to the steady state. The present version of the code is for the laminar flow of a perfect gas. The role of the adaptation algorithm in resolving various features of blunt body/wake flow for planetary entry conditions is emphasized. The algorithm is demonstrated on problems involving massive blowing from the surface of the Galileo probe and moderate blowing from the surface of a sphere.

Analysis of Rohrs counterexamples to adaptive control

Abstract: Simulation results by Rohrs indicate potential difficulties with adaptive control. Analysis which explains the nature of the difficulties and ways to avoid them are presented in this paper.

IIR algorithms for adaptive line enhancement

Abstract: In this paper we introduce a simple IIR structure for the adaptive line enhancer. Two algorithms based on gradient-search techniques are presented for adapting the structure. Results from experiments which utilized real data as well as computer simulations are provided.

A digital adaptive filter using a memory-accumulator architecture: Theory and realization

Abstract: This paper presents a novel approach to the construction of an adaptive filter making use of the so-called "distributed arithmetic" filter architecture originally suggested by Peled and Liu [7] for the realization of fixed response digital frequency filters. The technique uses only the operations of memory access, addition, and scaling, without the need for digital multiplication. Since multiplication is often quoted as the major bottleneck in digital signal processing structures, the system derives considerable advantage by the exclusion of this operation. The paper presents the derivation of a new adaptive algorithm based on this particular hardware structure, although no rigorous theoretical proof of the algorithm convergence properties is given. Computer simulations are included to demonstrate some of the basic operational characteristics of the structure. Finally, results from a hardware prototype, constructed using standard TTL integrated circuits, are presented. This approach differs from contemporary ideas which depend on the use of digital multipliers in either custom VLSI designs or using standard signal processing chips. It offers high-bandwidth operation at low cost using devices which are already in great demand by the computer market. Alternatively, the algorithm is ideal for implementation as a microprocessor-based system which could operate on real-time voice-bandwidth signals with a minimum of peripheral interface circuitry.

Convergence analysis of LMS and NLMS adaptive algorithms

Abstract: The main contribution of this paper is the unified treatment of convergence analysis for both LMS and NLMS adaptive algorithms. The following new results are obtained: (i) necessary and sufficient conditions of convergence, (ii) optimal adjustment gains and optimal convergence rates, (iii) interrelationship between LMS and NLMS gains, and (iv) non-stationary algorithm design.

Fast, fixed-order, least-squares algorithms for adaptive filtering

Abstract: Fast, fixed-order, exact-least-squares algorithms for tapped-delay-line adaptive-filtering applications are presented in this paper. These new recursive algorithms require fewer operations per iteration and exhibit better numerical properties than the so-called Fast-Kalman algorithm of Ljung and Falconer [1978] and the unnormalized, least-squares, joint-process-lattice algorithms of Morf and Lee [1978]. In comparison with the currently used stochastic-gradient or LMS adaptive algorithm of Widrow and Hoff, the new, fixed-order, least-squares algorithms yield substantial improvements in transient behavior at a modest increase in computational complexity. Additionally, over a wide range of practical applications, the new algorithms demonstrate numerical properties comparable to those of the normalized lattice introduced by Lee, Morf, and Friedlander [1981], but at a considerable reduction in complexity.

An Algebraic Approach to Superresolution Array Processing

Abstract: In a recent paper we made an algebraic characterization of the problem of resolving closely spaced plane waves incident on a linear array. The characterization encompassing several superresolution processing methods and encompassed the Wiener, maximum likelihood, and Pisarenko methods as well as suggesting new procedures. In this paper we amplify the algebraic approach and extend the results to consider correlated noise. An adaptive algorithm is given for a particularly effective processing method.

Friendly interfacing to simple speech recognizers

Abstract: We describe improvements to the recognition performance of a simple commercial speech recognizer. Topics include the selection of acoustically distinct words; a method of ‘training’ (storing utterances for later use as templates) which mimics the real task, and therefore reduces the difference in diction between training and task; the representation of variability in diction by storing repeated examples of each utterance separately, instead of using a simple statistical average; and the construction of an adaptive algorithm which updates its templates at appropriate moments. The results of empirical investigations with the adaptive algorithm show a very considerable improvement in performance. We argue that the development of speech recognizers has given the hardware undue attention, and that a rigorous attack on adaptive recognition, treated as a problem in control theory, would lead to a sophisticated interface to complement sophisticated hardware. The system we describe has.been successfully u...

Tracking characteristics of the Kalman filter in a nonstationary environment for adaptive filter applications

Abstract: In this paper, the Kalman filter theory is used to develop an algorithm for updating the tap-weight vector of an adaptive tapped-delay line filter that operates in a nonstationary environment. The tracking behaviour of the algorithm is discussed in detail. Computer simulation experiments show that this algorithm, unlike the exponentially weighted recursive least-squares (deterministic) algorithm, is always stable. Simulation results are included in the paper to illustrate this phenomenon.

An Adaptive Method for Minimizing a Sum of Squares of Nonlinear Functions

Abstract: The Gauss-Newton and the Levenberg-Marquardt algorithms for solving nonlinear least squares problems, minimize F(x) = sum_i=1^m (f_i(x))^2 for x in R^n, are both based upon the premise that one term in the Hessian of F(x) dominates its other terms, and that the Hessian may be approximated by this dominant term J^T J, where J_ij = ( delta f_i / delta x_j ). We are motivated here by the need for an algorithm which works well when applied to problems for which this premise is substantially violated, and is yet able to take advantage of situations where the premise holds. We describe and justify a method for approximating the Hessian of F ( x ) which uses a convex combination of J^T J and a matrix obtained by making quasi-Newton updates. In order to evaluate the usefulness of this idea, we construct a nonlinear least squares algorithm which uses this Hessian approximation, and report test results obtained by applying it to a set of test problems. A merit of our approach is that it demonstrates how a single adaptive algorithm can be used to efficiently solve unconstrained nonlinear optimization problems (whose Hessians have no particular structure), small residual and large residual, nonlinear least squares problems. Our paper can also be looked upon as an investigation for one problem area, of the following more general question: how can one combine two different Hessian approximations (or model functions) which are simultaneously available? The technique suggested here may thus be more widely applicable and may be of use, for example, when minimizing functions which are only partly composed of sums of squares arising in penalty function methods.

The filtered error LMS Algorithm

Abstract: In this paper, we show that the LMS algorithm fails to converge to an optimal solution in the presence of nonstationary noise. To overcome this problem, we introduce the filtered error LMS (FELMS) algorithm. The FELMS algorithm consists of two interconnected adaptive processes. The first process filters the error used by the other. Simulation results demonstrate the superiority of the FELMS configuration with respect to the regular LMS configuration.

Adaptive flutter suppression, analysis and test

Abstract: Methods of adaptive control have been applied to suppress a potentially violent flutter condition of a half-span model of a lightweight figher aircraft. This marked the confluence of several technologies with active flutter suppression, digital control and adaptive control theory the primary contributors. The control algorithm was required to adapt both to slowly varying changes, corresponding to changes in the flight condition or fuel loading and to rapid changes, corresponding to a store release or the transition from a stable to an unstable flight condition. The development of the adaptive control methods was followed by a simulation and checkout of the complete system and a wind tunnel demonstration. As part of the test, a store was released from the model wing tip, transforming the model abruptly from a stable configuration to a violent flutter condition. The adaptive algorithm recognized the unstable nature of the resulting configuration and implemented a stabilizing control law in a fraction of a second. The algorithm was also shown to provide system stability over a range of wind tunnel Mach numbers and dynamic pressures.

Time delay estimation using the adaptive delay element

Abstract: Time delay estimation between two narrowband signals is a problem common to applications such as seismic and acoustical signal processing. Using a simple real-time adaptive delay element, we compare delay estimation accuracy for both stationary and nonstationary time delays as well as for varying input signal-to-noise ratios. The comparison includes a modification to the adaptive algorithm which yields a smoothed delay estimate.

A Self-Tuning Feedback Controller

Abstract: The self-tuning feedback controller (SFC) described in this paper is mathematically and structurally equivalent to a conventional, discrete, PID, feedback controller. However, it also includes a simple estimation or adaptive algorithm that tunes the controller constants such that a quadratic performance index with a control weighting term is minimized. Global stability is mathematically proven in the presence of bounded, external unmeasured inputs. Application of the SFC to a pilot-plant evaporator demonstrates the practicality of this algorithm.

Source-oriented adaptive beamforming

Abstract: A system concept expected to improve sonar multiple target detection and classification performance in severe directional interference environments is presented. Adaptive nulling of undesired strong interference sources and detection of desired weak sources are achieved by utilizing constrained adaptive processing techniquestogether with conventional processing techniques. The adaptive processing functions are incorporated in the overall system architecture in a manner which maintains the computational complexity and storage requirementslinearly proportional to the product of the number of interference sources and the number of adaptive weights per interference source. In addition, a detailed discussion is provided concerning the importance of certaina priori assumptions on constrained adaptive algorithm performance.

Stochastic convergence properties of the adaptive gradient lattice

Abstract: A stochastic fixed-point theorem is used as a basis for the study of stochastic convergence properties (in mean-squares sense) of the adaptive gradient lattice filter. Such properties include conditions on the stepsize in the adaptive algorithm and analytic expressions for the misadjustment and convergence rate.

Error Models for Stable Hybrid Adaptive Systems

Abstract: Several error models are analyzed using two distinct approaches. Applications, extensions and modifications of the adaptive algorithms are treated. The principal steps in the proof of global stability of a hybrid adaptive control system are outlined. The same algorithms when suitably modified are shown to be applicable to both discrete and continuous systems with two time-scales. Simulation results are included.

Some Applications of Direct Adaptive Control to Large Structural Systems

Abstract: Direct multivariable model reference adaptive control (DMMRAC) applications are considered with a representative example of a large structural system (LSS). Such applications have in the past been shown to be feasible for multivariable systems provided that there exists a constant feedback gain matrix such that the resulting input-output transfer function is (simply) positive real.

HYBRID PROGRAMMABLt TRANSVERSAL FILTERS

Abstract: Advanced integrated coininunication, navigation, and identification (CNI) systems must be capable of providing increased immunity to interference signals in order to satisfy e xisting and future mission r equi rements. S pread spectrum techniques are effective in overcomiqg some electromagnetic interference, hut t here are practical limits to the l evel of interference suppression that these techniques can achieve. Adaptive filters can e nhance the anti jam performance of spread s pectrum systems b y selectively filtering unwanted signals and internal interference sources from the received spectrum. An adapti ve interference suppression (AIS) system breadboard has been developed which uses four LiNb03, complex tap weight configured prograrnmahle transversal filters (PTF) and adaptive algorithm techniques to implement an adaptive filter. A discussion of the device improvements, adaptive system and performance is presented.

Microprocessor based self-tuning regulator

Abstract: The applications of modern control theory on industrial processes are enhanced by the availability of microprocessors. These new processors encourage the control engineers to apply some of the techniques of stochastic control theory. Even through, these control techniques improve the cost index, their implementation has been impeded by the complexity of the control algorithm. New algorithms have been developed to suit the available microprocessor capabilities. This paper is concerned with the design and implementation of self- tuning adaptive algorithm. The controller is built around the Intel 8080 microprocessor. The software is designed to be modular. Three identification techniques as well as two control algorithms are available to build the self-tuning regulator. The identification techniques are; Least Squares (LS), Extended Least Squares (ELS) and Recursive Maximum Likelihood (RML). Control algorithms, available, are; Minimum Variance (MV) and Dead Beat Control (DB). An algorithm based on LS identification and MV control technique has been implemented and tested on a thermal process. Results obtained have demonstrated the superiority of the suggested technique over the conventional one.

Joint optimization of adaptive predictive coding.

Abstract: This work investigates the behavior of the residual signal in adaptive predictive coding (APC), which is one of the waveform encoding techniques. It also studies the variation of the predictor coefficients as a function of the structure of the predictor. Using continuous estimation of the predicted sample values of the speech signal, it is possible to force the residual signal to zero. Using the entropy of the continuously estimated prediction coefficients and of the residual, the structure of the adaptive algorithm is investigated. Results obtained using computer simulation show that the continuous estimation of predictor coefficients does result in a very low entropy for the residual distribution but predictor coefficients change dramatically from sample to sample, resulting in a very high entropy for the predictor coefficient distribution. Hence various different algorithms have been investigated to jointly optimize the APC. Prediction coefficients are changed only when the residual signal exceeds a particular value. Two different algorithms, which use this criterian to estimate the prediction coefficients are investigated. The results show that this represents a better approach towards reducing the number of bits/sample for the representation of the speech signal. The prediction coefficients do not change too much from sample to sample. It also keeps the residual distribution entropy low.

Adaptive compression of teleconference sequences using the LMS Algorithm

Abstract: The Least Mean Squares (LMS) adaptive algorithm is applied to the problem of reducing the mean square power of the information sequence required for transmission of video or teleconferencing image sequences. The results of processing actual teleconferencing sequences are presented and compared with the results of using a fixed coefficient predictive algorithm. Operating on actual teleconferencing sequences averaging 2% - 12% moving area, the LMS adaptive filter is shown to provide approximately 1-4 dB reduction in transmitted sequence power when compared to a fixed coefficient filter. Further, the performance of the LMS adaptive system is shown to be quite insensitive to the value of the feedback gain parameter, α, for a wide range of α. This allows the user to achieve a substantial performance gain (compared to the fixed coefficient system) without encountering problems of adaptive filter stability.