检索结果-内蒙古大学图书馆

Exploring biologically inspired shallow model for visual classification

SIGNAL PROCESSING 2014年 105卷 1-11页

作者： Tang, Tang Qiao, Hong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Visual classification has long been a major challenge for computer vision. In recent years, biologically inspired visual models have raised great interests. However, most of the related studies mainly focus on learning features and representations from very large scale dataset relying on deep network architecture, which is doomed to fail with limited training samples due to its high complexity. In this paper, it is found that aside from the deep architecture, two other biologically inspired mechanisms, the pooling and nonlinear operations, also contribute to the improvement of classification performance. Based on this perspective, a new classifier of shallow architecture is proposed, in which the both mechanisms are implemented with max operation. Moreover, the architecture is derived in a probabilistic perspective to further explain the underlying rationale thereof. To train the classifier, a supervised learning algorithm is devised to minimize the hinge loss function under the new architecture. Based on the manifold assumption of continuously transforming features, an unsupervised learning algorithm is also presented to learn the features used by the classifier. Finally, the method is compared against other classifiers on several image classification benchmarks. The results demonstrate the strength of the proposed method when the training data source is limited. (c) 2014 Elsevier B.V. All rights reserved.

关键词： Biologically inspired model Visual classification Max-pooling Shallow model

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Parallel Traffic management System and Its Application to the 2010 Asian Games

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2013年第1期14卷 225-235页

作者： Xiong, Gang Dong, Xisong Fan, Dong Zhu, Fenghua Wang, Kunfeng Lv, Yisheng Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Field data are important for convenient daily travel of urban residents, reducing traffic congestion and accidents, pursuing a low-carbon environment-friendly sustainable development strategy, and meeting the extra peak traffic demand of large sporting events or large business activities, etc. To meet the field data demand during the 2010 Asian (Para) Games held in Guangzhou, China, based on the novel Artificial systems, Computational experiments, and Parallel execution (ACP) approach, the Parallel Traffic management System (PtMS) was developed. It successfully helps to achieve smoothness, safety, efficiency, and reliability of public transport management during the two games, supports public traffic management and decision making, and helps enhance the public traffic management level from experience-based policy formulation and manual implementation to scientific computing-based policy formulation and implementation. The PtMS represents another new milestone in solving the management difficulty of real-world complex systems.

关键词： Artificial systems, Computational experiments, and Parallel execution (ACP) approach artificial transportation system (ATS) field data parallel system video detection

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Data-Driven Neuro-Optimal Temperature control of Water-Gas Shift Reaction Using Stable Iterative Adaptive Dynamic Programming

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2014年第11期61卷 6399-6408页

作者： Wei, Qinglai Liu, Derong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this paper, a novel data-driven stable iterative adaptive dynamic programming (ADP) algorithm is developed to solve optimal temperature control problems for water-gas shift (WGS) reaction systems. According to the system data, neural networks (NNs) are used to construct the dynamics of the WGS system and solve the reference control, respectively, where the mathematical model of the WGS system is unnecessary. Considering the reconstruction errors of NNs and the disturbances of the system and control input, a new stable iterative ADP algorithm is developed to obtain the optimal control law. The convergence property is developed to guarantee that the iterative performance index function converges to a finite neighborhood of the optimal performance index function. The stability property is developed to guarantee that each of the iterative control laws can make the tracking error uniformly ultimately bounded (UUB). NNs are developed to implement the stable iterative ADP algorithm. Finally, numerical results are given to illustrate the effectiveness of the developed method.

关键词： Adaptive critic designs adaptive dynamic programming (ADP) approximate dynamic programming approximation errors data-driven control neural networks (NNs) optimal control reinforcement learning water-gas shift (WGS)

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A Novel Dual Iterative Q-Learning Method for Optimal Battery management in Smart Residential Environments

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2015年第4期62卷 2509-2518页

作者： Wei, Qinglai Liu, Derong Shi, Guang Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this paper, a novel iterative Q-learning method called "dual iterative Q-learning algorithm" is developed to solve the optimal battery management and control problem in smart residential environments. In the developed algorithm, two iterations are introduced, which are internal and external iterations, where internal iteration minimizes the total cost of power loads in each period, and the external iteration makes the iterative Q-function converge to the optimum. Based on the dual iterative Q-learning algorithm, the convergence property of the iterative Q-learning method for the optimal battery management and control problem is proven for the first time, which guarantees that both the iterative Q-function and the iterative control law reach the optimum. Implementing the algorithm by neural networks, numerical results and comparisons are given to illustrate the performance of the developed algorithm.

关键词： Adaptive critic designs adaptive dynamic programming (ADP) approximate dynamic programming neural networks optimal control Q-learning smart grid

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Neural-Network-Based Nonlinear Model Predictive control for Piezoelectric Actuators

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2015年第12期62卷 7717-7727页

作者： Cheng, Long Liu, Weichuan Hou, Zeng-Guang Yu, Junzhi Tan, Min Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Piezoelectric actuators (PEAs) have been widely used in nanotechnology due to their characteristics of fast response, large mass ratio, and high stiffness. However, hysteresis, which is an inherent nonlinear property of PEAs, greatly deteriorates the control performance of PEAs. In this paper, a nonlinear model predictive control (NMPC) approach is proposed for the displacement tracking problem of PEAs. First, a "nonlinear autoregressive-moving-average with exogenous inputs" (NARMAX) model of PEAs is implemented by multilayer neural networks;second, the tracking control problem is converted into an optimization problem by the principle of NMPC, and then, it is solved by the Levenberg-Marquardt algorithm. The most distinguished feature of the proposed approach is that the inversion model of hysteresis is no longer a necessity, which avoids the inversion imprecision problem encountered in the widely used inversion-based control algorithms. To verify the effectiveness of the proposed modeling and control methods, experiments are made on a commercial PEA product (P-753.1CD, Physik Instrumente), and comparisons with some existing controllers and a commercial proportional-integral-derivative controller are conducted. Experimental results show that the proposed scheme has satisfactory modeling and control performance.

关键词： Neuralnetworks nonlinearautoregressive-moving-average with exogenous inputs (NARMAX) piezoelectric actuator (PEA) predictive control

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Vision-based initial weld point positioning using the geometric relationship between two seams

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2013年第9-12期66卷 1535-1543页

作者： Fang, Zaojun Xu, De Tan, Min Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This paper presents a novel vision-based initial weld point positioning method for the welding systems of container manufacture. The new method is based on the geometric relationship between the two seams at the two different stages of the whole welding task such as the initialization stage and the welding stage. The torch is aligned with the initial weld point manually at the first stage, and the image feature and the parameters of the seam line are computed. At the second stage, the target image feature of the seam line is firstly computed using the geometric relationship, then the alignment of the torch is automated based on the difference between the target and the current image features. The geometric relationship between the two seams is analyzed, and then the realization of the new method including the image processing, the computation of the parameters of the seam line, and the control system design is given in detail. Finally, experiments are well conducted to prove the effectiveness of the proposed initial weld point positioning method.

关键词： Vision Vision control Welding robot Initial weld point positioning Geometric relationship

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Neural-network-based adaptive optimal tracking control scheme for discrete-time nonlinear systems with approximation errors

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NEUROCOMPUTING 2015年第PartA期149卷 106-115页

作者： Wei, Qinglai Liu, Derong Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China

In this paper, a new infinite horizon neural-network-based adaptive optimal tracking control scheme for discrete-time nonlinear systems is developed. The idea is to use iterative adaptive dynamic programming (ADP) algorithm to obtain the iterative tracking control law which makes the iterative performance index function reach the optimum. When the iterative tracking control law and iterative performance index function in each iteration cannot be accurately obtained, the convergence criteria of the iterative ADP algorithm are established according to the properties with finite approximation errors. If the convergence conditions are satisfied, it shows that the iterative performance index functions can converge to a finite neighborhood of the lowest bound of all performance index functions. Properties of the finite approximation errors for the iterative ADP algorithm are also analyzed. Neural networks are used to approximate the performance index function and compute the optimal control policy, respectively, for facilitating the implementation of the iterative ADP algorithm. Convergence properties of the neural network weights are proven. Finally, simulation results are given to illustrate the performance of the developed method. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Adaptive dynamic programming Adaptive critic designs Approximate dynamic programming Value iteration Approximation errors Optimal tracking control

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Dual iterative adaptive dynamic programming for a class of discrete-time nonlinear systems with time-delays

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NEURAL COMPUTING & APPLICATIONS 2013年第7-8期23卷 1851-1863页

作者： Wei, Qinglai Wang, Ding Zhang, Dehua Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this paper, a new dual iterative adaptive dynamic programming (ADP) algorithm is developed to solve optimal control problems for a class of nonlinear systems with time-delays in state and control variables. The idea is to use the dynamic programming theory to solve the expressions of the optimal performance index function and control. Then, the dual iterative ADP algorithm is introduced to obtain the optimal solutions iteratively, where in each iteration, the performance index function and the system states are both updated. Convergence analysis is presented to prove the performance index function to reach the optimum by the proposed method. Neural networks are used to approximate the performance index function and compute the optimal control policy, respectively, for facilitating the implementation of the dual iterative ADP algorithm. Simulation examples are given to demonstrate the validity of the proposed optimal control scheme.

关键词： Adaptive dynamic programming Approximate dynamic programming Adaptive critic designs Optimal control Time-delay Nonlinear systems

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Adaptive Dynamic Programming for Optimal Tracking control of Unknown Nonlinear systems With Application to Coal Gasification

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2014年第4期11卷 1020-1036页

作者： Wei, Qinglai Liu, Derong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this paper, we establish a new data-based iterative optimal learning control scheme for discrete-time nonlinear systems using iterative adaptive dynamic programming (ADP) approach and apply the developed control scheme to solve a coal gasification optimal tracking control problem. According to the system data, neural networks (NNs) are used to construct the dynamics of coal gasification process, coal quality and reference control, respectively, where the mathematical model of the system is unnecessary. The approximation errors from neural network construction of the disturbance and the controls are both considered. Via system transformation, the optimal tracking control problem with approximation errors and disturbances is effectively transformed into a two-person zero-sum optimal control problem. A new iterative ADP algorithm is then developed to obtain the optimal control laws for the transformed system. Convergence property is developed to guarantee that the performance index function converges to a finite neighborhood of the optimal performance index function, and the convergence criterion is also obtained. Finally, numerical results are given to illustrate the performance of the present method.

关键词： Adaptive dynamic programming coal gasification data-based control finite approximation errors neural networks optimal tracking control

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Neural-network-based robust optimal control design for a class of uncertain nonlinear systems via adaptive dynamic programming

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INFORMATION SCIENCES 2014年 282卷 167-179页

作者： Wang, Ding Liu, Derong Li, Hongliang Ma, Hongwen Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this paper, the neural-network-based robust optimal control design for a class of uncertain nonlinear systems via adaptive dynamic programming approach is investigated. First, the robust controller of the original uncertain system is derived by adding a feedback gain to the optimal controller of the nominal system. It is also shown that this robust controller can achieve optimality under a specified cost function, which serves as the basic idea of the robust optimal control design. Then, a critic network is constructed to solve the Hamilton-Jacobi-Bellman equation corresponding to the nominal system, where an additional stabilizing term is introduced to verify the stability. The uniform ultimate boundedness of the closed-loop system is also proved by using the Lyapunov approach. Moreover, the obtained results are extended to solve decentralized optimal control problem of continuous-time nonlinear interconnected large-scale systems. Finally, two simulation examples are presented to illustrate the effectiveness of the established control scheme. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Adaptive dynamic programming Approximate dynamic programming Decentralized optimal control Neural network Robust optimal control

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