A non-linear iterative learning control approach is developed for application to stroke rehabilitation. The subject is seated in a robotic workstation and electrical stimulation is applied to their triceps muscle to a...
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Accelerated Norm-Optimal Iterative Learning control (NOILC) is a recently developed method to improve the convergence performance of the well known NOILC algorithm. This paper investigates the effectiveness of this me...
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ISBN:
(纸本)9781846000386
Accelerated Norm-Optimal Iterative Learning control (NOILC) is a recently developed method to improve the convergence performance of the well known NOILC algorithm. This paper investigates the effectiveness of this method experimentally on a gantry robot facility, which has been extensively used to test a wide range of linear model based ILC algorithms. The results obtained confirm that the accelerated algorithm outperforms NOILC algorithm and in particular, the improvements at initial stage can be substantial, which is of great interest in practical applications.
control of the networked control systems is a crucial challenge. In the environment of shared communication media, data may be randomly delayed or even lost. As the shared communication links are used in the critical ...
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ISBN:
(纸本)9783902661913
control of the networked control systems is a crucial challenge. In the environment of shared communication media, data may be randomly delayed or even lost. As the shared communication links are used in the critical infrastructure systems, the problem becomes even more important. As opposed to network compensation, solutions presented in the paper integrate network with the controlled plant. The proposed networked control system employs the model predictive control technology and is based on the buffer in the actuator. Stability of the presented solution used to control a linear discrete time system is analyzed. A simulation example is also provided.
The subject of this paper is modeling of the influence of non-minimum phase plant dynamics on the performance possible from gradient based norm optimal iterative learning control algorithms. It is established that per...
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The subject of this paper is modeling of the influence of non-minimum phase plant dynamics on the performance possible from gradient based norm optimal iterative learning control algorithms. It is established that performance in the presence of right-half plane plant zeros typically has two phases. These consist of an initial fast monotonic reduction of the L 2 error norm followed by a very slow asymptotic convergence. Although the norm of the tracking error does eventually converge to zero, the practical implications over finite trials is apparent convergence to a non-zero error. The source of this slow convergence is identified and a model of this behavior as a (set of) linear constraint(s) is developed. This is shown to provide a good prediction of the magnitude of error norm where slow convergence begins. Formulae for this norm are obtained for single-input single-output systems with several right half plane zeroes using Lagrangian techniques and experimental results are given that confirm the practical validity of the analysis.
A simulation approach was proposed to analyze the spectra of Doppler signals from stenoed artery in a steady flow, which can provide a useful guidance for detecting the formation understanding, diagnosis and treatment...
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A simulation approach was proposed to analyze the spectra of Doppler signals from stenoed artery in a steady flow, which can provide a useful guidance for detecting the formation understanding, diagnosis and treatment of many cardiovascular diseases. Firstly, by solving Navier-Stokes equations, the velocity distributions in the vessels with various stenoed degrees are calculated. Secondly, power spectral density (PSD) of the Doppler signals is estimated according to the relationship of velocity profiles and power spectral density. Finally, Doppler signals are generated using cosine-superposed methods. The results show that the spectra of Doppler signals are similar to those found in practice. Therefore, the proposed approach is useful for simulating the spectra of Doppler ultrasound signals from stenoed artery in a steady flow.
A simulation approach was proposed to analyze the spectra of Doppler signals in local expansion artery, which can provide a useful guidance for detecting the formation and growth progress of the aneurysms using the Do...
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A simulation approach was proposed to analyze the spectra of Doppler signals in local expansion artery, which can provide a useful guidance for detecting the formation and growth progress of the aneurysms using the Doppler ultrasound technology and forecasting the size of aneurysm. Firstly, by solving Navier-Stokes equations, the velocity distributions in the vessels with various expansion degrees are calculated. Secondly, power spectral density (PSD) of the Doppler signals is estimated according to the relationship of velocity profiles and power spectral density. Finally, Doppler signals are generated using cosine-superposed methods. The results show that the spectra of Doppler signals are similar to those found in practice. Therefore, the proposed approach is useful for simulating the spectra of Doppler ultrasound signals.
Within the class of nonlinear system models, the so-called block-oriented models have gained wide recognition and attention by the system identification and automaticcontrol community. Typically, these models are con...
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It is the mainstream method that in human face detection and recognition with AdaBoost as the representative based on statistical learning method. Detection rates have reached a high level, and can achieve real-time d...
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ISBN:
(纸本)9781424472352
It is the mainstream method that in human face detection and recognition with AdaBoost as the representative based on statistical learning method. Detection rates have reached a high level, and can achieve real-time detection. However, AdaBoost algorithm treats equally for different categories, there is no distinction between the cost of the different error categories. This paper presents a new cost-sensitive Ada Boost-based face detection algorithm, ensuring the detection rate and speed, effectively reducing the false detection rate, and improving the detection accuracy.
The expectation maximisation (EM) algorithm has proven to be effective for a range of identification problems. Unfortunately, the way in which the EM algorithm has previously been applied has proven unsuitable for the...
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ISBN:
(纸本)9781424477456
The expectation maximisation (EM) algorithm has proven to be effective for a range of identification problems. Unfortunately, the way in which the EM algorithm has previously been applied has proven unsuitable for the commonly employed innovations form model structure. This paper addresses this problem, and presents a previously unexamined method of EM algorithm employment. The results are profiled, which indicate that a hybrid EM/gradient-search technique may in some cases outperform either a pure EM or a pure gradient-based search approach.
This paper presents a novel approach to the estimation of a general class of dynamic nonlinear system models. The main contribution is the use of a tool from mathematical statistics, known as Fishers' identity, to...
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ISBN:
(纸本)9781424477456
This paper presents a novel approach to the estimation of a general class of dynamic nonlinear system models. The main contribution is the use of a tool from mathematical statistics, known as Fishers' identity, to establish how so-called "particle smoothing" methods may be employed to compute gradients of maximum-likelihood and associated prediction error cost criteria.
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