Recently, maximum margin clustering (MMC) has been proposed for a cross-view action recognition. However, such a method neglects the temporal relationship between contiguous frames in the same action video. In this pa...
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Recently, maximum margin clustering (MMC) has been proposed for a cross-view action recognition. However, such a method neglects the temporal relationship between contiguous frames in the same action video. In this paper we propose a novel method called contextual maximum margin clustering (CMMC) to tackle cross-view action recognition. In CMMC, we add temporal regularization to give a high penalty when the contiguous frames are dissimilar. Thus, the CMMC not only achieves the goal of finding maximum margin hyperplanes, but also explicitly considers the temporal information among contiguous frames. Our method is verified on the IXMAS dataset and the experimental results demonstrate that our method can achieve better performance than the state-of-the-art methods.
In this paper, using a neural-network-based online learning optimal control approach, a novel decentralized control strategy is developed to stabilize a class of continuous-time nonlinear interconnected large-scale sy...
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In this paper, using a neural-network-based online learning optimal control approach, a novel decentralized control strategy is developed to stabilize a class of continuous-time nonlinear interconnected large-scale systems. First, optimal controllers of the isolated subsystems are designed with cost functions reflecting the bounds of interconnections. Then, it is proven that the decentralized control strategy of the overall system can be established by adding appropriate feedback gains to the optimal control policies of the isolated subsystems. Next, an online policy iteration algorithm is presented to solve the Hamilton-Jacobi-Bellman equations related to the optimal control problem. Through constructing a set of critic neural networks, the cost functions can be obtained approximately, followed by the control policies. Furthermore, the dynamics of the estimation errors of the critic networks are verified to be uniformly and ultimately bounded. Finally, a simulation example is provided to illustrate the effectiveness of the present decentralized control scheme.
We aim to build an integrated fixturing model to describe the structural properties and thermal properties of the support frame of glass laser optics. Therefore, (a) a near global optimal set of clamps can be computed...
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We aim to build an integrated fixturing model to describe the structural properties and thermal properties of the support frame of glass laser optics. Therefore, (a) a near global optimal set of clamps can be computed to minimize the surface shape error of the glass laser optic based on the proposed model, and (b) a desired surface shape error can be obtained by adjusting the clamping forces under various environmental temperatures based on the model. To construct the model, we develop a new multiple kernel learning method and call it multiple kernel support vector functional regression. The proposed method uses two layer regressions to group and order the data sources by the weights of the kernels and the factors of the layers. Because of that, the influences of the clamps and the temperature can be evaluated by grouping them into different layers. (C) 2014 Optical Society of America
In this paper, we develop an online synchronous approximate optimal learning algorithm based on policy iteration to solve a multiplayer nonzero-sum game without the requirement of exact knowledge of dynamical systems....
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In this paper, we develop an online synchronous approximate optimal learning algorithm based on policy iteration to solve a multiplayer nonzero-sum game without the requirement of exact knowledge of dynamical systems. First, we prove that the online policy iteration algorithm for the nonzero-sum game is mathematically equivalent to the quasi-Newton's iteration in a Banach space. Then, a model neural network is established to identify the unknown continuous-time nonlinear system using input-output data. For each player, a critic neural network and an action neural network are used to approximate its value function and control policy, respectively. Our algorithm only needs to tune the weights of critic neural networks, so there will be less computational complexity during the learning process. All the neural network weights are updated online in real-time, continuously and synchronously. Furthermore, the uniform ultimate bounded stability of the closed-loop system is proved based on Lyapunov approach. Finally, two simulation examples are given to demonstrate the effectiveness of the developed scheme.
Piezoelectric actuators (PEAs) are widely used in high-precision positioning applications. However, the inherent hysteresis nonlinearity seriously deteriorates the tracking performance of PEAs. To deal with it, the co...
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Piezoelectric actuators (PEAs) are widely used in high-precision positioning applications. However, the inherent hysteresis nonlinearity seriously deteriorates the tracking performance of PEAs. To deal with it, the compensation of the hysteresis by using its inverse model (called inversion-based) is the popular method in the literature. One major disadvantage of this method is that the tracking performance of PEAs highly relies on its inverse model. Meanwhile, the computational burden of obtaining the inverse model is overwhelming. In addition, the physical constraints of the input voltage of PEAs is hardly handled by the inversion-based method. This paper proposes an inversion-free predictive controller, which is based on a dynamic linearized multilayer feedforward neural network (MFNN) model. By the proposed method, the inverse model of the inherent hysteresis is not required, and the control law can be obtained in an explicit form. By using the technique of constrained quadratic programming, the proposed method still works well when dealing with the physical constraints of PEAs. Moreover, an error compensation term is introduced to reduce the steady-state error if the dynamic linearized MFNN cannot approximate the PEA's dynamical model satisfactorily. To verify the effectiveness of the proposed method, experiments are conducted on a commercial PEA. The experiment results show that the proposed method has a satisfactory tracking performance even with high-frequency references. Comparisons demonstrate that the proposed method outperforms some existing results.
In this paper, multiperson zero-sum differential games for a class of continuous-time uncertain nonlinear systems are solved using a new iterative adaptive dynamic programming (ADP) algorithm. The idea is to use ADP t...
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In this paper, multiperson zero-sum differential games for a class of continuous-time uncertain nonlinear systems are solved using a new iterative adaptive dynamic programming (ADP) algorithm. The idea is to use ADP technique to obtain the optimal control pair iteratively that makes the performance index function reach the optimal solution of the zero-sum differential games without the system model. It proves that the iterative performance index functions are convergent to the optimal solution of the game. Stability properties of the system under the iterative control pairs are also presented. Neural networks are used to build the system model, approximate the performance index function, and compute the optimal control policy, respectively, for facilitating the implementation of the iterative ADP method. Finally, two simulation examples are given to demonstrate the performance of the proposed method. Copyright (c) 2012 John Wiley & Sons, Ltd.
This letter aims to learn a global representation for each point in a random cluster using only purely local geometric or topological information. Based on this, distributed tags for indoor positioning break the atomi...
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This letter aims to learn a global representation for each point in a random cluster using only purely local geometric or topological information. Based on this, distributed tags for indoor positioning break the atomicity of tags and make deployment more arbitrary. It also allows NP-hard matches to be quickly estimated with only one local observation. The novel self-supervised topological representation learning method only takes local point clusters as input and utilizes the proposed cluster-based sampling, training, and loss functions to form global self-comparison. The training samples are generated in real-time virtually, and there are few matching errors after being transferred to practice. The compact backbone network directly processes the coordinates of points and abandons the iterative optimization commonly used in matching. Moreover, it uses the representation to measure similarity directly, and the inference speed reaches the millisecond level. In the actual and virtual experiments, the local point clusters are surprisingly accurately matched to the random global ones. The localization based on this is also verified, and the relevant results prove the effectiveness of the proposed method.
In this paper, we solve the zero-sum game problems for discrete-time affine nonlinear systems with known dynamics via iterative adaptive dynamic programming algorithm. First, a greedy heuristic dynamic programming ite...
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In this paper, we solve the zero-sum game problems for discrete-time affine nonlinear systems with known dynamics via iterative adaptive dynamic programming algorithm. First, a greedy heuristic dynamic programming iteration algorithm is developed to solve the zero-sum game problems, which can be used to solve the Hamilton-Jacobi-Isaacs equation associated with H-infinity optimal regulation control problems. The convergence analysis in terms of value function and control policy is provided. To facilitate the implementation of the algorithm, three neural networks are used to approximate the control policy, the disturbance policy, and the value function, respectively. Then, we extend the algorithm to R, optimal tracking control problems through system transformation. Finally, two simulation examples are presented to demonstrate the effectiveness of the proposed scheme. (C) 2013 Elsevier B.V. All rights reserved.
Welcome to the first issue of the IEEE Transactions on Computational Social systems (TCSS) for 2018, and Happy New Year to everyone. According to the Chinese lunar calendar, this is the year of the Dog, which in Chine...
Welcome to the first issue of the IEEE Transactions on Computational Social systems (TCSS) for 2018, and Happy New Year to everyone. According to the Chinese lunar calendar, this is the year of the Dog, which in Chinese culture represents trust, loyalty, dedication, and energy. As such, I would like to take this opportunity to express my best wishes of a happy, healthy, and high-producing 2018 to each and every one of our readers, reviewers, and editors.
In this paper, a data-based method is developed for analyzing the controllability and observability of discrete-time linear systems in noisy environment. This method uses measured data to estimate the controllability ...
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In this paper, a data-based method is developed for analyzing the controllability and observability of discrete-time linear systems in noisy environment. This method uses measured data to estimate the controllability matrix and the observability matrix without identifying system models. The unbiasedness and consistency of this estimate with measurement noise and system noise are proven, respectively. As the estimated error of system parameters will not accumulate in calculating the controllability matrix and observability matrix, this method has a higher precision than traditional methods, especially in high-dimensional state space. In the simulation, the advantages of the data-based method in accuracy and convergence are illustrated. (C) 2014 Elsevier Inc. All rights reserved.
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