In this paper, we propose a new recursive subspace model identification (RSMI) based on regression and natural power method (NP) which is an array signal processing algorithm with excellent convergence properties. We ...
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In this paper, we propose a new recursive subspace model identification (RSMI) based on regression and natural power method (NP) which is an array signal processing algorithm with excellent convergence properties. We call this new algorithm as ‘R-NP'. The basic idea of the algorithm is to utilize an unstructured least squares linear regression approach at the updating observation vector step and the close relationship between RSMI with NP. This algorithm has simpler procedures than other RSMI algorithms. A numerical example illustrates that R-NP method is efficient and have a better performance in terms of transient behavior with respect to EIVPAST. In this paper, we consider the case where the order of system to be identified is a priori known.
This paper is concerned with the recursive estimation of autoregressive models, in particular the realtime determination of the order of such a specification. A model selection criterion based on the minimum descripti...
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This paper is concerned with the recursive estimation of autoregressive models, in particular the realtime determination of the order of such a specification. A model selection criterion based on the minimum description length principle due to Rissanen is discussed and strong consistency in the stationary situation is shown. Alternative criteria are also considered and modifications required to introduce forgetting when the procedures are implemented in the non-stationary case are presented. Some simulation evidence on the performance of the criteria when applied to stationary and non-stationary processes is given.
The parameter estimation problem in systems governed by sto chastic partial differential equations is discussed. A recursive algo rithm is derived to identify space-dependent parameters in a class of distributed syste...
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The parameter estimation problem in systems governed by sto chastic partial differential equations is discussed. A recursive algo rithm is derived to identify space-dependent parameters in a class of distributed systems driven by random inputs. The algorithm uses noisy observations taken over a finite number of equidistant observation points located in a spatial domain. The estimates obtained are r.m.s. - optimal due to the assumptions about the original system and the noise. Simula tion studies reveal good convergence of the algorithm. This algorithm was computer realized for parabolic, hyperbolic, and the Helmholtz equa tions. Simulation results show its undisputable advantages in compare to the algorithm of Kubrusly and Curtain derived for the same situation, since it lends itself more readily to digital computer realization and the estimates obtained converge at a faster rate. For systems with con stant parameters the computation procedure is much simplified.
On-line tool wear estimation in turning is essential for on-line cutting process optimization. In this paper, an adaptive observer based on cutting force measurement is used for a reliable on-line flank wear estimatio...
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On-line tool wear estimation in turning is essential for on-line cutting process optimization. In this paper, an adaptive observer based on cutting force measurement is used for a reliable on-line flank wear estimation and tool life monitoring. The design of the adaptive observer is realized using a linear observer and a recursive Instrumental Variable method as the adaptation algorithm. A continuous time hybrid identification approach is used. For model validation, the flank wear is estimated using a nonlinear model.
作者：
Yutaka MaedaKansai University
Faculty of Engineering Department of Electrical Engineering 3-3-35 Yamate-cho Suita Osaka 564 JAPAN
In this paper, we propose recursive algorithms using time difference simultaneous perturbation to find a minimum or maximum point of a function. Instead of a gradient vector used in the well-known gradient method, the...
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In this paper, we propose recursive algorithms using time difference simultaneous perturbation to find a minimum or maximum point of a function. Instead of a gradient vector used in the well-known gradient method, the algorithms need only one value of the function for every modification of an estimator. The algorithms are very simple. Therefore, applications of these algorithms to many problems such as control of dynamical system or learning rule of neural networks are very easy. A numerical simulation is shown. The paper contains a comparison with the random search, the finite difference and the simultaneous perturbation method.
With the wide application of complex resistivity method, searching for precise and rapid forward and inversion algorithms has become the focus of complex resistivity research. In the paper we have developed a finite e...
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With the wide application of complex resistivity method, searching for precise and rapid forward and inversion algorithms has become the focus of complex resistivity research. In the paper we have developed a finite element algorithm for computing the complex resistivity response of general two-dimensional models. Triangular element is used in the finite element method (FEM). The simplified boundary condition on infinite boundary is employed to improve the speed of calculation. The modeling takes two steps. Firstly, we compute apparent complex resistivity values for four different frequencies by FEM. Then, apparent Cole-Cole parameters are derived from these apparent complex resistivity values using a recursive algorithm. The algorithm has been checked with one-dimensional algorithm. Two typical two-dimensional polarization models are used by the forward approach. The apparent complex resistivity and the apparent Cole-Cole parameters maps show that there are distinct anomaly features in the pseudo-sections of apparent complex resistivity and apparent Cole-Cole parameters.
This paper presents two new families of the generalized Ball curves which include the Be′zier curve, the generalized Ball curves defined by Wang and Said independently and some intermediate curves. The relative degre...
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This paper presents two new families of the generalized Ball curves which include the Be′zier curve, the generalized Ball curves defined by Wang and Said independently and some intermediate curves. The relative degree elevation and reduction schemes, recursive algorithms and the Bernstein-Be′zier representation are also given.
Water vapor plumes emanating from the geyser vents at Enceladus's south pole area invite the possibility of direct access to the subsurface liquid reservoir to acquire pristine biological material if it exists. An...
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Water vapor plumes emanating from the geyser vents at Enceladus's south pole area invite the possibility of direct access to the subsurface liquid reservoir to acquire pristine biological material if it exists. Any descending lander adapted for plume localization is required to not only explore the icy plume environment during its descent, but it must also infer the location of the landing target-the plume source-autonomously. Compared with existing scenarios of terrestrial plume source localization methods, the source likelihood map (SLIM) method for an Enceladus mission offers a more extensive search area, a higher maneuver velocity, and a shorter search time. This paper investigates a particle-based odor source localization (pOSL) approach that offers the prospect of targeting one of the plume sources by autonomously measuring the concentration field. Reasons for the negative likelihood and overfitting issues associated with Bayesian SLIM are analyzed to build a novel probabilistic model. By implementing this model, the proposed pOSL algorithm evaluates the observation likelihood via posterior maximization method and estimates the source location via the sequential Monte Carlo method. The pOSL algorithm resolves difficulties associated with other methods while reducing the time complexity from O(N tau) to O(N). The numerical simulations illustrate that the proposed approach is feasible and permits accurate targeting of Enceladus's geyser vents.
A large number of fully normalized associated Legendre function (fnALF) calculations are required to compute Earth's gravity field elements using ultra high-order gravity field coefficient models. In the surveying...
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A large number of fully normalized associated Legendre function (fnALF) calculations are required to compute Earth's gravity field elements using ultra high-order gravity field coefficient models. In the surveying and mapping industry, researchers typically rely on CPU-based systems for these calculations, which leads to limitations in execution speed and power efficiency. Although modern CPUs improve instruction execution efficiency through instruction-level parallelism, the constraints of a shared memory architecture impose further limitations on the execution speed and power efficiency. This results in exponential increases in computation time as demand rises alongside high power consumption. In this article, we present a new computational implementation of an fnALF based on the ZYNQ platform. We design a task-parallel "pipeline" architecture which converts the original serial logic into a more efficient hardware implementation, and we utilize a redundant calculation layer to handle repetitive coefficient computations separately. The experimental results demonstrate that our system achieved accurate and rapid calculations. Under the only one geocentric residual latitude condition, we measured the computation times for spherical harmonic coefficient degrees of 360, 720, and 1080 to be 0.155922 s, 0.520950 s, and 1.401609 s, respectively. In the case of the multiple geocentric residual latitudes condition, our design generally yielded efficiency gains of over three times those of MATLAB R2020b implementation. Additionally, our calculated results were used to determine the geoid height in the field with an error of less than +/- 0.1m, confirming the reliability of our computations.
This paper discusses the identification of a special kind of nonlinear systems: bilinear systems. A new formulation of discrete bilinear systems is developed, and an improved recursive identification algorithm for the...
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This paper discusses the identification of a special kind of nonlinear systems: bilinear systems. A new formulation of discrete bilinear systems is developed, and an improved recursive identification algorithm for these systems is presented. A theorem proving the convergence of the algorithm is established. Results of the simulation demonstrate the effectiveness of the proposed algorithm.
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