In order to meet the increasing demand for electric vehicles, automotive suppliers such as ZF Friedrichshafen AG are trying to develop modular electric motor platforms. In order to find the optimal platform and to be ...
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A method that can identify the rotational inertia in a timely manner is proposed because the performance of a permanent magnet synchronous motor servo system is easily affected by the rotational inertia. Firstly, prin...
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A key assumption in the theory of nonlinear adaptive control is that the uncertainty of the system can be expressed in the linear span of a set of known basis functions. While this assumption leads to efficient algori...
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ISBN:
(纸本)9781665436601
A key assumption in the theory of nonlinear adaptive control is that the uncertainty of the system can be expressed in the linear span of a set of known basis functions. While this assumption leads to efficient algorithms, it limits applications to very specific classes of systems. We introduce a novel nonparametric adaptive algorithm that learns an infinite-dimensional parameter density to cancel an unknown disturbance in a reproducing kernel Hilbert space. Surprisingly, the resulting control input admits an analytical expression that enables its implementation despite its underlying infinite-dimensional structure. While this adaptive input is rich and expressive – subsuming, for example, traditional linear parameterizations – its computational complexity grows linearly with time, making it comparatively more expensive than its parametric counterparts. Leveraging the theory of random Fourier features, we provide an efficient randomized implementation which recovers the computational complexity of classical parametric methods while provably retaining the expressiveness of the nonparametric input. In particular, our explicit bounds only depend polynomially on the underlying parameters of the system, allowing our proposed algorithms to efficiently scale to high-dimensional systems. As an illustration of the method, we demonstrate the ability of the algorithm to learn a predictive model for a 60-dimensional system consisting of ten point masses interacting through Newtonian gravitation.
A class of parameter-free online linear optimization algorithms is proposed that harnesses the structure of an adversarial sequence by adapting to some side information. These algorithms combine the reduction techniqu...
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The semi-random graph process is a single player game in which the player is initially presented an empty graph on n vertices. In each round, a vertex u is presented to the player independently and uniformly at random...
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The quaternion Gaussian kernel is usually used when solving quaternion nonlinear problems. However, how to choose a proper value of kernel width is still an important issue. In most previous studies, the kernel width ...
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The quaternion Gaussian kernel is usually used when solving quaternion nonlinear problems. However, how to choose a proper value of kernel width is still an important issue. In most previous studies, the kernel width was set manually or estimated in advance by using Silvermans rule based on the sample distribution, which can easily degrade the performance of algorithms. In this brief, the variable kernel width quaternion kernel least mean squares algorithm (VKW-QKLMS) aims to develop an online technique for optimizing the kernel width of the quaternion kernel LMS (QKLMS) algorithm, in which the filter weight and the kernel width are alternately updated by using stochastic gradient algorithm. Simulation results show that the performance of the VKW-QKLMS algorithm does not depend on the selection of initial value of the kernel width. The VKW-QKLMS algorithm can usually achieve better performances than other competing algorithms. Only when the kernel width in the QKLMS algorithm is empirically selected as a proper value, the QKLMS algorithm can achieve much the same performance as that of the VKW-QKLMS algorithm.
A key assumption in the theory of nonlinear adaptive control is that the uncertainty of the system can be expressed in the linear span of a set of known basis functions. While this assumption leads to efficient algori...
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The adaptive algorithm of detection and selection of isolated compact objects on monochrome images in remote sensing systems is investigated. To characterize compactness, the ratio of the object perimeter squared to i...
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We study the problem of estimating the number of edges in an n-vertex graph, accessed via the Bipartite Independent Set query model introduced by Beame et al. (TALG '20). In this model, each query returns a Boolea...
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The traditional unscented kalman filter (UKF) algorithm is used to estimate the state of charge (SOC) of the lithium battery, it has better estimation effect, and less amount of calculation. However, this algorithm al...
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