作者:
Keran ChenTian ZhouBoping LiuState Key Laboratory of Chemical Engineering
East China University of Science and Technology Meilong Road 130 Shanghai 200237 Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Meilong Road 130 Shanghai 200237
In this paper, self-triggered consensus control problem is investigated for a class of multi-agent systems. Each agent computes its next update time instance at the predefined time. This control strategy extends subst...
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ISBN:
(纸本)9781479913329;9781479940998
In this paper, self-triggered consensus control problem is investigated for a class of multi-agent systems. Each agent computes its next update time instance at the predefined time. This control strategy extends substantially the existing work on event-triggered control, because in the self-triggered deployment the agents have no need to maintain the track of the state error that triggers the actuation between continuous update instants. A distributed self-triggered control procedure is presented for a special first-order multi-agents and is shown to achieve the rendezvous objective. The effectiveness of the proposed control method is illustrated by a numerical example.
作者:
Yu CheZhou TianBoping LiuState Key Laboratory of Chemical Engineering
East China University of Science and Technology Meilong Road 130 Shanghai 200237 Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai 200237 P.R.China
Filtering and feature extraction are very important in the analysis and study of EEG signal under +Gz *** this study,a new filter of different frequency characteristics of EEG signal under +Gz acceleration is construc...
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ISBN:
(纸本)9781467329705
Filtering and feature extraction are very important in the analysis and study of EEG signal under +Gz *** this study,a new filter of different frequency characteristics of EEG signal under +Gz acceleration is constructed and four kinds of rhythms of EEG signal are extracted by using wavelet packet *** under different G loads is analyzed and compared,and then EEG dynamic characteristics are studied in order to analyze its advantages and *** results show that wavelet packet method can effectively suppress interference bands in EEG,such as EMG,power and so on,and effectively reflect the dynamic characteristics of different rhythms,exhibiting good *** proposed method is also applicable for analyzing and studying other dynamic biomedical signals.
This paper focuses on the problem of adaptive output feedback stabilization for a class of stochastic nonlinear system with unknown control directions. By using a linear state transformation, the unknown control coeff...
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This paper focuses on the problem of adaptive output feedback stabilization for a class of stochastic nonlinear system with unknown control directions. By using a linear state transformation, the unknown control coefficients are lumped together, such that the original system is transformed to a new system for which control design becomes feasible. By employing the input-driven observer, a novel adaptive neural network (NN) output-feedback controller which only contains one adaptive parameter is developed for such systems by using backstepping technique and NNs' parameterization. The proposed control design guarantees that all the signals in the closed-loop systems are 4-moment semi-globally uniformly ultimately bounded.
An automatic reference selection method with prejudgments was developed in order to identify the two patterns of diffuse and localized EEG activity,as well as the topographical *** diffuse activity is identified and t...
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ISBN:
(纸本)9781467329705
An automatic reference selection method with prejudgments was developed in order to identify the two patterns of diffuse and localized EEG activity,as well as the topographical *** diffuse activity is identified and the effect of adopted reference is analyzed by using two *** localized activity is analyzed where the focal and distributed electrodes are derived based on an iterative detection ***,the suitable reference is determined which is adaptive to the actual characteristics of diffuse and localized ***,the topographical distribution is obtained automatically during the reference selection *** presented method has the performance to be an assistant and subjective tool for clinical application,especially for the quantitative interpretation on EEG rhythm.
In this paper, the synchronization of a nonlinear network with time-varying coupling delay is investigated via distributed impulsive control. Our objective is to design the distributed impulsive controller with minimu...
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In this paper, the synchronization of a nonlinear network with time-varying coupling delay is investigated via distributed impulsive control. Our objective is to design the distributed impulsive controller with minimum coupling strength such that the nonlinear network with coupling delay is globally exponentially synchronous. Some sufficient conditions have been derived based on Lyapunov-Razumikhin method in terms of matrix inequalities. An example is presented to illustrate the effectiveness of the proposed control methods.
Semi-supervised dimensionality reduction is an important research area for data classification. A new linear dimensionality reduction approach, global inference preserving projection (GIPP), was proposed to perform ...
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Semi-supervised dimensionality reduction is an important research area for data classification. A new linear dimensionality reduction approach, global inference preserving projection (GIPP), was proposed to perform classification task in semi-supervised case. GIPP provided a global structure that utilized the underlying discriminative knowledge of unlabeled samples. It used path-based dissimilarity measurement to infer the class label information for unlabeled samples and transformd the diseriminant algorithm into a generalized eigenequation problem. Experimental results demonstrate the effectiveness of the proposed approach.
A discrete artificial bee colony algorithm is proposed for solving the blocking flow shop scheduling problem with total flow time criterion. Firstly, the solution in the algorithm is represented as job permutation. Se...
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A discrete artificial bee colony algorithm is proposed for solving the blocking flow shop scheduling problem with total flow time criterion. Firstly, the solution in the algorithm is represented as job permutation. Secondly, an initialization scheme based on a variant of the NEH (Nawaz-Enscore-Ham) heuristic and a local search is designed to construct the initial population with both quality and diversity. Thirdly, based on the idea of iterated greedy algorithm, some newly designed schemes for employed bee, onlooker bee and scout bee are presented. The performance of the proposed algorithm is tested on the well-known Taillard benchmark set, and the computational results demonstrate the effectiveness of the discrete artificial bee colony algorithm. In addition, the best known solutions of the benchmark set are provided for the blocking flow shop scheduling problem with total flow time criterion.
The ammonia synthesis reactor is the core unit in the whole ammonia synthesis production. The ammonia concentration at the ammonia converter outlet is a significant process variable, which reflects directly the produc...
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The ammonia synthesis reactor is the core unit in the whole ammonia synthesis production. The ammonia concentration at the ammonia converter outlet is a significant process variable, which reflects directly the production efficiency. However, it is hard to be measured reliably online in real applications. In this paper, a soft sensor based on BP neural network (BPNN) is applied to estimate the ammonia concentration. A modified group search optimization with nearest neighborhood (GSO-NH) is proposed to optimize the weights and thresholds of BPNN. GSO-NH is integrated with BPNN to build a soft sensor model. Finally, the soft sensor model based on BPNN and GSO-NH (GSO-NH-NN) is used to infer the outlet ammonia concentration in a real-world application. Three other modeling methods are applied for comparison with GSO-NH-NN. The results show that the soft sensor based on GSO-NH-NN has a good prediction performance with high accuracy. Moreover, the GSO-NH-NN also provides good generalization ability to other modeling problems in ammonia synthesis production.
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