Showing papers by "John B. Moore published in 1990"
TL;DR: In this article, the authors used a first-order, finite-state, discrete-time Markov process to extract small, single channel ion currents from background noise, which can be used to detect signals that do not conform to a firstorder Markov model, but the method is less accurate when the background noise is not white.
Abstract: Techniques for extracting small, single channel ion currents from background noise are described and tested. It is assumed that single channel currents are generated by a first-order, finite-state, discrete-time, Markov process to which is added `white' background noise from the recording apparatus (electrode, amplifiers, etc.). Given the observations and the statistics of the background noise, the techniques described here yield a posteriori estimates of the most likely signal statistics, including the Markov model state transition probabilities, duration (open- and closed-time) probabilities, histograms, signal levels, and the most likely state sequence. Using variations of several algorithms previously developed for solving digital estimation problems, we have demonstrated that: (1) artificial, small, first-order, finite-state, Markov model signals embedded in simulated noise can be extracted with a high degree of accuracy, (2) processing can detect signals that do not conform to a first-order Markov model but the method is less accurate when the background noise is not white, and (3) the techniques can be used to extract from the baseline noise single channel currents in neuronal membranes. Some studies have been included to test the validity of assuming a first-order Markov model for biological signals. This method can be used to obtain directly from digitized data, channel characteristics such as amplitude distributions, transition matrices and open- and closed-time durations.
188 citations
TL;DR: In this paper, a nonlinear generalization of the Youla-Kucera parametrization for nonlinear systems is presented, and the equivalence of the class of all (bounded-input) stabilizing nonlinear pre- and feedback-compensators to a class of possibly unstable feedback controllers is shown.
59 citations
TL;DR: The task of finding a class of balanced minimal realizations is shown to be equivalent to finding limiting solutions of certain gradient flow differential equations, and convergence is rapid and numerical stability properties are attractive.
41 citations
05 Dec 1990
TL;DR: In this paper, the authors developed a robust stabilization theory for nonlinear plants using the left coprime factorizations of the plant and controller under certain differential boundedness assumptions, and showed that a necessary and sufficient condition for K/sub Q/ to stabilize G/sub S/ with Q, S not necessarily stable, is that S stabilizes Q.
Abstract: The authors describe steps toward the development of a robust stabilization theory for nonlinear plants. An approach using the left coprime factorizations of the plant and controller under certain differential boundedness assumptions is used. Attention is focused on a characterization of the class of all stabilizing nonlinear controllers K/sub Q/ for a nonlinear plant G, parameterized in terms of an arbitrary stable (nonlinear) operator Q. Also considered is the dual class of all plants G/sub S/ stabilized by a given nonlinear controller K and parameterized in terms of an arbitrary stable (nonlinear) operator S. It is shown that a necessary and sufficient condition for K/sub Q/ to stabilize G/sub S/ with Q, S not necessarily stable, is that S stabilizes Q. This robust stabilization result is of interest for the solution of problems in the areas of nonlinear adaptive control and simultaneous stabilization. >
35 citations
TL;DR: In this article, the authors developed a robust stabilization theory for nonlinear plants using the left coprime factorizations of the plant and controller under certain differential boundedness assumptions. But their work is limited to the problem of nonlinear adaptive control and simultaneous stabilization.
34 citations
TL;DR: In this article, the class of strictly proper stabilizing controllers for proper linear plants can be structured as state estimate feedback with frequency shaping in the state estimates and/or in the State Estimate Feedback law, where the selection of where the frequency shaping takes place is at the designer's discretion.
Abstract: It is shown that the class of all strictly proper stabilizing controllers for proper linear plants can be structured as state estimate feedback with frequency shaping in the state estimates and/or in the state estimate feedback law. The selection of where the frequency shaping takes place is at the designer's discretion. The parameterization of the controller class can be in terms of an arbitrary proper stable transfer function, with the closed-loop system transfer functions affine in the transfer function. With constant output feedback permitted in addition to the state estimate feedback, the class of all proper stabilizing controllers can be generated in a like manner. >
27 citations
19 citations
TL;DR: Extended least squares ELS algorithms are proposed for ARMAX model identification with the objective of avoiding the positive real condition associated with standard equation error and output error algorithms by an overparametrization at the cost of additional richness requirements on excitation signals.
Abstract: Extended least squares (ELS) algorithms are proposed for ARMAX model identification with the objective of avoiding the positive real condition associated with standard equation error and output error algorithms. This is achieved by an overparametrization at the cost of additional richness requirements on excitation signals, but without introducing ill-conditioning or infinite dimensional calculations as in earlier methods. Results for the case of D-step-ahead prediction ELS algorithms for ARMAX models also explored in the paper.
8 citations
TL;DR: In this paper, a general class of constrained Hm optimization problems is considered and a sequence of smooth optimization problems are shown to be solvable by standard optimization software packages such as those available in the NAG or lMSL library.
Abstract: SUMMARY In Hm optimal control the cost function is the maximum singular value of a transfer function matrix over a frequency range. The optimization is over all stabilizing controllers. In constrained Hm control the controllers typically have a fixed structure, perhaps conveniently parametrized in terms of a parameter vector. Also, there may be functional constraints involving singular values representing, for example, robustness requirements. Such problems are usually cast as non-smooth optimization problems. In this paper we consider a general class of constrained Hm optimization problems and show that these problems can be approximated by a sequence of smooth optimization problems, Thus each of the approximate problems is readily solvable by standard optimization software packages such as those available in the NAG or lMSL library. The proposed approach via smooth optimization is simple in terms of mathematical content, easy to implement and computationally efficient.
5 citations
TL;DR: In this paper, an adaptive resonance suppression algorithm for high-order unmodelled dynamics is presented. But it is not suitable for control systems with a fixed controller and the adaptive loop augments the fixed controller feedback loop.
Abstract: Control systems can drift into instability or, less catastrophically, exhibit resonance behaviour. One role for adaptive controllers is to learn sufficient information concerning the dominant closed-loop system mode so as to apply effective feedback to dampen these modes. In such situations the adaptive loop augments the fixed controller feedback loop. This paper presents an algorithm for adaptive resonance suppression and provides simulation results to study its behaviour in the presence of high-order unmodelled dynamics. The algorithm appears particularly useful for enhancing existing fixed controller designs.
23 May 1990
TL;DR: In this paper, the concept of functional persistence of excitation (PE) was introduced and associated with functional uniform complete observability (UCO) for linear systems, which is a natural generalization of the corresponding finite dimensional result.
Abstract: Adaptive systems involving function learning can be formulated in terms of integral equations of the first kind, possibly with separable, finite-dimensional kernels. The learnig process involves estimating the influence functions [2]. To achieve convergence of the influence function estimates and exponentially stability, it is important to have persistence of excitation in the training tasks. This paper develops the concept of functional persistence of excitation (PE), and the associated concept of functional uniform complete observability (UCO). Relevant PE and UCO properties for linear systems are developed. For example, a key result is that uniform complete observability in this context is maintained under bounded integral operator output injection?a natural generalization of the corresponding finite dimensional result. This paper also demonstrates the application of the theory to linear error equations associated with a repetitive control algorithm.
TL;DR: In this article, extended least squares (ELS) for ARMAX model identification of continuous-time and certain discrete-time systems were proposed, which have a relaxed strictly positive real (SPR) conditi...
Abstract: This paper proposes extended least-squares (ELS) for ARMAX model identification of continuous-time and certain discrete-time systems. The schemes have a relaxed strictly positive real (SPR) conditi ...
TL;DR: In this article, modified algorithms which involve the introduction of noise into the calculations are proposed and studied by theory and simulations, which can be seen as an approach to adaptive estimation /control when there are jump parameter changes which include order changes.
TL;DR: This paper proposes Extended Least Squares schemes for ARMAX model identification of continuous-time systems with a relaxed Strictly Positive Real condition for global convergence and state conditions for the persistence of excitation of the regression vectors in the proposed ELS schemes to assure strong consistency and obtain convergence rates.
TL;DR: An algorithm for adaptive resonance suppression is presented and simulation results to study its behaviour in the presence of high-order un modelled dynamics and it appears useful for enhancing existing fixed controller designs.
TL;DR: In this article, the authors consider the performance of a stabilizing causal controller for a causal nominal plant G (possibly time varying), with controller augmentations including an adaptive stable subsystem Q. The objective is to search on-line in terms of Q and state space descriptions of G, Ko.
05 Dec 1990
TL;DR: The authors propose causal schemes with delay that asymptotically achieve signal model identification and optimal signal statistics and online re-estimation formulae that reduce memory requirements and improve computational processing speed and the adaptive capabilities of hidden Markov model estimation schemes.
Abstract: A commonly used hidden Markov model signal processing scheme that obtains certain optimal signal statistics and estimates is the forward-backward algorithm. This is a noncausal fixed-interval scheme. Repeated application of this algorithm, along with the Baum Welch re-estimation formulae, allows optimal estimation of the signal model parameters, including signal levels, level transition probabilities, and noise statistics. The authors propose causal schemes with delay that asymptotically achieve signal model identification and optimal signal statistics. The key features of these schemes are sawtooth processing and online re-estimation formulae. The intention is to significantly reduce memory requirements and improve computational processing speed and the adaptive capabilities of hidden Markov model estimation schemes. >