In this paper, we use a noise transfer function to filter the input-output data and propose a new recursive algorithm for multiple-input single-output systems under the maximum likelihood principle. The main contribut...
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In this paper, we use a noise transfer function to filter the input-output data and propose a new recursive algorithm for multiple-input single-output systems under the maximum likelihood principle. The main contributions of this paper are to derive a filtering based maximum likelihood recursive least squares (F-ML-RLS) algorithm for reducing computational burden and to present two recursive least squares algorithms to show the effectiveness of the F-ML-RLS algorithm. In the end, an illustrative simulation example is provided to test the proposed algorithms and we show that the F-ML-RLS algorithm has a high computational efficiency with smaller sizes of its covariance matrices and can produce more accurate parameter estimates. (C) 2015 Elsevier Inc. All rights reserved.
The modal controller of single-input system cannot stabilize the defective system with positive real part of repeated eigenvalues, because some of the generalized modes are uncontrollable. In order to stabilize the un...
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The modal controller of single-input system cannot stabilize the defective system with positive real part of repeated eigenvalues, because some of the generalized modes are uncontrollable. In order to stabilize the uncontrollable modes with positive real part of eigenvalues, the multi-input system should be introduced. This paper presents a recursive procedure for designing the feedback controller of the multi-input system with defective repeated eigenvalues. For a nearly defective system, we first transform it into a defective one, and apply the same method to manage. The proposed methods are based on the modal coordinate equations, to avoid the tedious mathematic manipulation. As an application of the presented procedure, two numerical examples are given at end of the paper.
Sparse Volterra model (sVM) is defined as a Volterra model (VM) that contains only a subset of its all possible model coefficients corresponding to its significant inputs and the existing terms of those inputs. Compar...
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ISBN:
(纸本)9781424441242
Sparse Volterra model (sVM) is defined as a Volterra model (VM) that contains only a subset of its all possible model coefficients corresponding to its significant inputs and the existing terms of those inputs. Compared with ordinary VM, sVM is more efficient and interpretable in representing sparsely connected multiple-input systems, e.g., neuronal networks. In this paper, we formulate a rigorous statistical method of estimating sVM based on the group L1-regularization. It allows simultaneous selection and estimation of the significant groups of coefficients of a VM and results in a sVM. Simulation results show that the actual structure of a sVM can be faithfully recovered even with short input-output data. This method can be extended and applied to the identification of the functional connectivity between neurons.
This paper proposes a method of estimating the HRTF (head-related transfer function) to shorten its measurement time by applying a system identification method. Since each directional HRTF has conventionally had to be...
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This paper proposes a method of estimating the HRTF (head-related transfer function) to shorten its measurement time by applying a system identification method. Since each directional HRTF has conventionally had to be measured one by one, a larger number of directions results in a longer HRTF measurement time. Since multidirectional HRTFs can also be treated as an MISO (multiple-input single-output) system, the proposed method estimates them simultaneously by a PEM (prediction error method). Experimental results indicated that the proposed method shortens the measurement time for HRTFs of 61 directions by a factor of 280.
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