This paper gives a tutorial overview of instrumental variable methods. Comparisons are made to the least-squares method. An analysis including consistency and asymptotic distribution of the parameter estimates is incl...
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This paper gives a tutorial overview of instrumental variable methods. Comparisons are made to the least-squares method. An analysis including consistency and asymptotic distribution of the parameter estimates is included.
A digital design for piecewise-linear (PWL) approximation to the sigmoid function is presented. Circuit operation is based on a recursive algorithm that uses lattice operators max and min to approximating nonlinear fu...
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A digital design for piecewise-linear (PWL) approximation to the sigmoid function is presented. Circuit operation is based on a recursive algorithm that uses lattice operators max and min to approximating nonlinear functions. The resulting hardware is programmable, allowing for the control of the delay-time/approximation-accuracy rate.
Motivated by the recent developments in digital diffusion networks, this work is devoted to the rates of convergence issue for a class of global optimization algorithms. By means of weak convergence methods, we show t...
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Motivated by the recent developments in digital diffusion networks, this work is devoted to the rates of convergence issue for a class of global optimization algorithms. By means of weak convergence methods, we show that a sequence of suitably scaled estimation errors converges weakly to a diffusion process (a solution of a stochastic differential equation). The scaling together with the stationary covariance of the limit diffusion process gives the desired rates of convergence. Application examples are also provided for some image estimation problems.
We describe the construction of accurate panoramic mosaics from multiple images taken with a rotating camera, or alternatively of a planar scene. The novelty of the approach lies in (i) the transfer of photogrammetric...
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We describe the construction of accurate panoramic mosaics from multiple images taken with a rotating camera, or alternatively of a planar scene. The novelty of the approach lies in (i) the transfer of photogrammetric bundle adjustment techniques to mosaicing;(ii) a new representation of image line measurements enabling the use of lines in camera self-calibration, including computation of the radial and other non-linear distortion;and (iii) the application of the variable state dimension filter to obtain efficient sequential updates of the mosaic as each image is added. We demonstrate that our method achieves better results than the alternative approach of optimising over pairs of images. (C) 2002 Elsevier Science B.V. All rights reserved.
In this paper,a new multi-dimentional recursive algorithm is given for multi-input, multi-output ARMAX systems. Satisfactory results can all be obtained in many simulation experiments.
ISBN:
(纸本)0780372689
In this paper,a new multi-dimentional recursive algorithm is given for multi-input, multi-output ARMAX systems. Satisfactory results can all be obtained in many simulation experiments.
In this paper, we propose a simple learning algorithm for non\|linear function approximation and system modeling using minimal radial basis function neural network with high generalization performance. A hybrid algori...
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In this paper, we propose a simple learning algorithm for non\|linear function approximation and system modeling using minimal radial basis function neural network with high generalization performance. A hybrid algorithm is constructed, which combines recursive n \|means clustering algorithm with a simple recursive regularized least squares algorithm (SRRLS). The n \|means clustering algorithm adjusts the centers of the network, while the SRRLS constructs a parsimonious network which makes the generalization performance of the network well. The SRRLS algorithm needs no matrix computing, so it has a lower computational cost and no ill\|conditional problem. Because of the recursive manner, this algorithm is suitable for on\|line applications. The effectiveness of this algorithm is demonstrated by two benchmark examples.
We describe the construction of accurate panoramic mosaics from multiple images taken with a rotating camera, or alternatively of a planar scene. The novelty of the approach lies in (i) the transfer of photogrammetric...
详细信息
We describe the construction of accurate panoramic mosaics from multiple images taken with a rotating camera, or alternatively of a planar scene. The novelty of the approach lies in (i) the transfer of photogrammetric bundle adjustment techniques to mosaicing;(ii) a new representation of image line measurements enabling the use of lines in camera self-calibration, including computation of the radial and other non-linear distortion;and (iii) the application of the variable state dimension filter to obtain efficient sequential updates of the mosaic as each image is added. We demonstrate that our method achieves better results than the alternative approach of optimising over pairs of images. (C) 2002 Elsevier Science B.V. All rights reserved.
In this paper, we study the H ∞ optimal filtering for multiparameter singularly perturbed system (MSPS). In order to obtain the solution, we must solve the multiparameter algebraic Riccati equations (MARE) with indef...
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In this paper, we study the H ∞ optimal filtering for multiparameter singularly perturbed system (MSPS). In order to obtain the solution, we must solve the multiparameter algebraic Riccati equations (MARE) with indefinite sign quadratic term. First, the existence of a unique and bounded solution of such MARE is newly proven. The main results in this paper are to propose a new recursive algorithm for solving the MARE and to find sufficient conditions regarding the convergence of our proposed algorithm. Using the recursive algorithm, we show that the solution of the MARE converges to a positive semi-definite stabilizing solution with the rate of convergence of O (|| μ || i +1 ).
The joint problem of the recursive estimation of an optimal predictor for the closed-loop system and the unbiased parameter estimation of an ARMAX plant model in closed-loop operation is considered, A special reparame...
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The joint problem of the recursive estimation of an optimal predictor for the closed-loop system and the unbiased parameter estimation of an ARMAX plant model in closed-loop operation is considered, A special reparameterized optimal predictor for the closed-loop is: introduced. This allows a parameter estimation algorithm for the plant model to be derived which is globally asymptotically stable in a deterministic environment and gives asymptotically unbiased parameters estimates under richness conditions.
An order-recursive algorithm is proposed to solve the 3-D Yule-Walker equations of causal 3-D AR models. It is computationally efficient and can be easily transformed into a computer program. Moreover, it can be utili...
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An order-recursive algorithm is proposed to solve the 3-D Yule-Walker equations of causal 3-D AR models. It is computationally efficient and can be easily transformed into a computer program. Moreover, it can be utilized to determine the orders of a causal 3-D AR process.
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