The prospect of helping disabled patients by translating neural activity from the brain into control signals for prosthetic devices is currently being realized. Initial proof-of-concept systems have demonstrated the n...
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ISBN:
(纸本)0780375793
The prospect of helping disabled patients by translating neural activity from the brain into control signals for prosthetic devices is currently being realized. Initial proof-of-concept systems have demonstrated the need for faster and more accurate estimation algorithms, without requiring unrealistically many neurons. To address this need, we recently reported the plan-movement maximum likelihood (PMML) algorithm. It combines plan activity, specifying reach end-point, with movement activity, specifying instantaneous direction and speed of the arm movement, to yield more accurate estimates with fewer neurons. This approach could greatly benefit from an improved ability to track the switching of plan activity, which precedes movement onset, so that a more accurate plan estimate can be incorporated into movement decoding. In this paper, we propose a modified point-process filter, employing an adaptive parameter, that is capable of more accurately tracking constant plan periods and step changes than conventional methods. We also suggest that this algorithm is more attractive than an alternate maximum likelihood step tracking scheme. Ultimately, the adaptive algorithm is well-suited for use with the PMML algorithm, or for directly controlling prosthetic devices with plan activity, and should improve neural prosthetic system performance.
A new decoding method is presented for linear analog encoders enabling major improvements in both accuracy and resolution. A simulation study is used to demonstrate the performance improvement of the proposed method, ...
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A new decoding method is presented for linear analog encoders enabling major improvements in both accuracy and resolution. A simulation study is used to demonstrate the performance improvement of the proposed method, demonstrating that the new method can generate position estimates with accuracy about three times better than that of standard methods. Moreover, in some special cases, the resulting position accuracy can reach subnanometer levels, thus enabling further size reduction in the semiconductor industry. The proposed algorithm also yields velocity estimates better by about two orders of magnitude than those obtained with standard methods.
A new approach to technical condition monitoring of power units of aircraft and ship engines is suggested. It enables with the help of additional processing of two rotational velocity sensor outputs to get the informa...
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A new approach to technical condition monitoring of power units of aircraft and ship engines is suggested. It enables with the help of additional processing of two rotational velocity sensor outputs to get the information about variations of controlled process base parameters. The mathematical model of a transmission of a turboprop aircraft engine is deduced. Synthesis of adaptive algorithm of elastic shaft torsion angle estimation is developed. The task of turbine?s torque identification with the aim engine control improving and (or) for control of fatigue deterioration degree of the shaft?s material is considered. The possibility of the torsion angle adaptive estimation using for look-ahead cut-out of the engine in the case of a dangerous situation leading to destruction of the transportation is considered.
In this article, we examine the effect of constraints on estimation and control methods based on quadratic penalty functions. We begin with estimation theory and analyze how constraints alter the statistical propertie...
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In this article, we examine the effect of constraints on estimation and control methods based on quadratic penalty functions. We begin with estimation theory and analyze how constraints alter the statistical properties of the least squares estimates. It is shown that constraints can be used to formulate maximum likelihood (MLE) and maximum a posteriori (MAP) estimators for a variety of unimodal distributions. This provides greater flexibility over the assumption of normality inherent in the MLE and MAP interpretation of traditional least squares. We discuss how these ideas apply to state space models of dynamic systems. Possible applications for controllers that handle constraints are also discussed. A parameter estimation example is given to demonstrate the potential for improved performance over unconstrained least squares. (C) 2002 Elsevier Science Ltd. All rights reserved.
This paper presents an adaptive regulation scheme for a class of ordinary nonlinear nonautonomous second-order differential equations which includes as particular cases a number of particular differential equations wh...
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This paper presents an adaptive regulation scheme for a class of ordinary nonlinear nonautonomous second-order differential equations which includes as particular cases a number of particular differential equations which occur in applications. The unforced reference model is proposed to be a stable differential parametrization within the general class dealt with. Therefore, some sufficient Lyapunov's stability conditions for such a class are previously investigated which can be used, in particular, to set an appropriate reference model. The resulting closed-loop adaptive scheme is proved to be stable and it involves a parameter estimation scheme of least-squares type which is proved to possess all suitable properties in terms of estimates boundedness and asymptotic convergence of the estimates to finite limits as well as time-integrability of the squared adaptation error.
作者:
Tsodikov, AUniv Utah
Huntsman Canc Inst Div Biostat Salt Lake City UT 84112 USA
A flexible class of semi-parametric survival models is proposed that takes account of long- and short-term covariate effects in cancer survival. The diversity of responses described by the models include non-proportio...
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A flexible class of semi-parametric survival models is proposed that takes account of long- and short-term covariate effects in cancer survival. The diversity of responses described by the models include non-proportional and crossing survival curves as well as a fraction of long-term survivors. Restricted non-parametric maximum likelihood estimation procedures (RNPMLE) are developed to provide point estimates, confidence intervals and tests for the models. Numerical algorithms to fit semi-parametric survival models are emphasized. The methods are applied to analyse post-treatment survival of breast cancer patients diagnosed in Utah by age and stage. Copyright (C) 2002 John Wiley Sons, Ltd.
The paper deals with a problem of numerical conditioning of basic J – lossless factori-sations associated with suboptimal H ∞ – norm estimation of discrete-time processes described in the so-called delta-domain. St...
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The paper deals with a problem of numerical conditioning of basic J – lossless factori-sations associated with suboptimal H ∞ – norm estimation of discrete-time processes described in the so-called delta-domain. State space formulae for dual J – lossless factorisations of a chain scattering representation of the estimated process are given. Solutions are obtained via solving two coupled algebraic Riccati equations. A relative condition number of the delta-domain algebraic Riccati equation is employed as a measure of numerical conditioning of these solutions. A numerical example is given to show that solutions obtained for the delta operator are much better-conditioned than its counterpart versions based on the common forward shift operator.
An RF amplifier under compression is measured using a multi-carrier signal covering the band from 500MHz to 1.5GHz, using the vectorial network analyzer for nonlinear systems (NVNA). Based on this measurement, a Wiene...
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ISBN:
(纸本)0780381246
An RF amplifier under compression is measured using a multi-carrier signal covering the band from 500MHz to 1.5GHz, using the vectorial network analyzer for nonlinear systems (NVNA). Based on this measurement, a Wiener-Hammerstein model for this device is extracted. The model captures the wide band properties of the DUT up to 1.4% error inside the excitation band.
Linear transforms are often used for adaptation to test data in speech recognition systems. However, when used with small amounts of test data, these techniques provide limited improvements if any. This paper proposes...
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ISBN:
(纸本)0780374029
Linear transforms are often used for adaptation to test data in speech recognition systems. However, when used with small amounts of test data, these techniques provide limited improvements if any. This paper proposes a two-step Bayesian approach where a) the transforms lie in a subspace obtained at training time and b) the expansion coefficients of the transform are obtained using MAP. estimation algorithms are given for adaptation transforms for means, covariances, and feature spaces. Experimental results indicate that our method gives a significant improvement in performance over other methods.
Wiener-Hammerstein systems consist of a linear dynamic system followed by a static nonlinearity, followed by another linear dynamic system. These models are difficult to identify due to the presence of two dynamic sys...
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Wiener-Hammerstein systems consist of a linear dynamic system followed by a static nonlinearity, followed by another linear dynamic system. These models are difficult to identify due to the presence of two dynamic systems. Usually, a nonlinear estimation procedure is used to estimate the parameters of the different parts. This nonlinear estimation procedure needs good starting values to converge quickly and/or reliably to a global minimum. This paper proposes a method to compute a first estimate based on one measurement record only.
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