An adaptive neural network output-feedback regulation approach is proposed for a class of multi-input-multi-output nonlinear time-varying delayed systems.Both the designed observer and controller are free from time de...An adaptive neural network output-feedback regulation approach is proposed for a class of multi-input-multi-output nonlinear time-varying delayed systems.Both the designed observer and controller are free from time delays.Different from the existing results,this paper need not the assumption that the upper bounding functions of time-delay terms are known,and only a neural network is employed to compensate for all the upper bounding functions of time-delay terms,so the designed controller procedure is more simplified.In addition,the resulting closed-loop system is proved to be semi-globally ultimately uniformly bounded,and the output regulation error converges to a small residual set around the origin.Two simulation examples are provided to verify the effectiveness of control scheme.展开更多
A neural-network-based adaptive gain scheduling backstepping sliding mode control（NNAGS-BSMC） approach for a class of uncertain strict-feedback nonlinear system is proposed.First, the control problem of uncertain st...A neural-network-based adaptive gain scheduling backstepping sliding mode control（NNAGS-BSMC） approach for a class of uncertain strict-feedback nonlinear system is proposed.First, the control problem of uncertain strict-feedback nonlinear systems is formulated. Second, the detailed design of NNAGSBSMC is described. The sliding mode control（SMC） law is designed to track a referenced output via backstepping technique.To decrease chattering result from SMC, a radial basis function neural network（RBFNN） is employed to construct the NNAGSBSMC to facilitate adaptive gain scheduling, in which the gains are scheduled adaptively via neural network（NN）, with sliding surface and its differential as NN inputs and the gains as NN outputs. Finally, the verification example is given to show the effectiveness and robustness of the proposed approach. Contrasting simulation results indicate that the NNAGS-BSMC decreases the chattering effectively and has better control performance against the BSMC.展开更多
For the first time, an adaptive backstepping neural network control approach is extended to a class of stochastic non- linear output-feedback systems. Different from the existing results, the nonlinear terms are assum...For the first time, an adaptive backstepping neural network control approach is extended to a class of stochastic non- linear output-feedback systems. Different from the existing results, the nonlinear terms are assumed to be completely unknown and only a neural network is employed to compensate for all unknown nonlinear functions so that the controller design is more simplified. Based on stochastic LaSalle theorem, the resulted closed-loop system is proved to be globally asymptotically stable in probability. The simulation results further verify the effectiveness of the control scheme.展开更多
A new equalization method is proposed in this paper for severely nonlinear distorted channels. The structure of decision feedback is adopted for the non-singleton fuzzy regular neural network that is trained by gradie...A new equalization method is proposed in this paper for severely nonlinear distorted channels. The structure of decision feedback is adopted for the non-singleton fuzzy regular neural network that is trained by gradient-descent algorithm. The model shows a much better performance on anti-jamming and nonlinear classification, and simulation is carried out to compare this method with other nonlinear channel equalization methods. The results show the method has the least bit error rate （BER）.展开更多
在资源受限的水声网络中,使用软频率复用技术和自适应资源分配技术可以提高网络容量和能量效率。然而,水声信道的长传播时延和时变特性导致用于自适应技术的反馈信道状态信息(Channel State Information, CSI)是时变且过时的。非理想的...在资源受限的水声网络中,使用软频率复用技术和自适应资源分配技术可以提高网络容量和能量效率。然而,水声信道的长传播时延和时变特性导致用于自适应技术的反馈信道状态信息(Channel State Information, CSI)是时变且过时的。非理想的反馈CSI会降低自适应系统的性能。针对该问题,提出了一种基于多智能体深度Q网络的资源分配(Multi-agent Deep Q Network Based Resource Allocation, MADQN-RA)方法。该方法将水声软频率复用网络视为多智能体系统,并使用过时的反馈CSI序列作为系统状态。通过建立有效的奖励表达式,智能体可以跟踪时变时延水声信道的变化特性并做出相应的资源分配决策。为了进一步提高智能体的决策准确度,同时避免状态空间维度增大时的部分学习成本,结合动态状态长度方法改进了MADQN-RA。仿真结果表明,所提方法实现的系统性能优于基于其他学习的方法和基于信道预测的方法,且更接近理论最优值。展开更多
基金supported by the National Natural Science Foundation of China (60804021)the Fundamental Research Funds for the Central Universities (JY10000970001)
文摘An adaptive neural network output-feedback regulation approach is proposed for a class of multi-input-multi-output nonlinear time-varying delayed systems.Both the designed observer and controller are free from time delays.Different from the existing results,this paper need not the assumption that the upper bounding functions of time-delay terms are known,and only a neural network is employed to compensate for all the upper bounding functions of time-delay terms,so the designed controller procedure is more simplified.In addition,the resulting closed-loop system is proved to be semi-globally ultimately uniformly bounded,and the output regulation error converges to a small residual set around the origin.Two simulation examples are provided to verify the effectiveness of control scheme.
基金supported by the National Natural Science Foundation of China(11502288)the Natural Science Foundation of Hunan Province(2016JJ3019)+1 种基金the Aeronautical Science Foundation of China(2017ZA88001)the Scientific Research Project of National University of Defense Technology(ZK17-03-32)
文摘A neural-network-based adaptive gain scheduling backstepping sliding mode control（NNAGS-BSMC） approach for a class of uncertain strict-feedback nonlinear system is proposed.First, the control problem of uncertain strict-feedback nonlinear systems is formulated. Second, the detailed design of NNAGSBSMC is described. The sliding mode control（SMC） law is designed to track a referenced output via backstepping technique.To decrease chattering result from SMC, a radial basis function neural network（RBFNN） is employed to construct the NNAGSBSMC to facilitate adaptive gain scheduling, in which the gains are scheduled adaptively via neural network（NN）, with sliding surface and its differential as NN inputs and the gains as NN outputs. Finally, the verification example is given to show the effectiveness and robustness of the proposed approach. Contrasting simulation results indicate that the NNAGS-BSMC decreases the chattering effectively and has better control performance against the BSMC.
基金supported by the National Natural Science Foundation of China (60804021)
文摘For the first time, an adaptive backstepping neural network control approach is extended to a class of stochastic non- linear output-feedback systems. Different from the existing results, the nonlinear terms are assumed to be completely unknown and only a neural network is employed to compensate for all unknown nonlinear functions so that the controller design is more simplified. Based on stochastic LaSalle theorem, the resulted closed-loop system is proved to be globally asymptotically stable in probability. The simulation results further verify the effectiveness of the control scheme.
文摘A new equalization method is proposed in this paper for severely nonlinear distorted channels. The structure of decision feedback is adopted for the non-singleton fuzzy regular neural network that is trained by gradient-descent algorithm. The model shows a much better performance on anti-jamming and nonlinear classification, and simulation is carried out to compare this method with other nonlinear channel equalization methods. The results show the method has the least bit error rate （BER）.
基金Supported by National Natural Science Foundation of China（60474029 60774045 60634020 61075065） the Graduate Degree Thesis Innovation Foundation of Central South University
文摘水声网络(underwater acoustic network,UAN)具有长传播时延、高误码率、半双工通信等特性,这些特性严重影响了UAN中数据的可靠传输。而在线喷泉码具有在线控制、编解码复杂度低、码率自适应等诸多优势,在线喷泉码适合于保障UAN中数据的可靠传输。针对递归与限制反馈的在线喷泉码(recursive OFC with limited feedback,ROFC-LF)存在不理想覆盖和4元环问题导致略高的开销和频繁的反馈,提出适用于UAN的基于优先级与可Zigzag解码的ROFC-LF(priority-based and zigzag-decodable ROFC-LF,P-ZROFC-LF)。P-ZROFC-LF在建立阶段选取具有最高优先级的原始包进行编码直至所有原始包均参与编码。同时,引入可Zigzag解码编码,将无用编码包进行移位异或转换为有用编码包来提高解码性能。通过随机图理论,分析P-ZROFC-LF所需编码包数与原始包数之间的关系。理论分析与仿真结果表明,与大部分在线喷泉码相比,P-ZROFC-LF显著提高了反馈和开销性能。其中P-ZROFC-LF相比于ROFC-LF的反馈和开销分别减少了18%和0.0176,更适用于UAN。
文摘在资源受限的水声网络中,使用软频率复用技术和自适应资源分配技术可以提高网络容量和能量效率。然而,水声信道的长传播时延和时变特性导致用于自适应技术的反馈信道状态信息(Channel State Information, CSI)是时变且过时的。非理想的反馈CSI会降低自适应系统的性能。针对该问题,提出了一种基于多智能体深度Q网络的资源分配(Multi-agent Deep Q Network Based Resource Allocation, MADQN-RA)方法。该方法将水声软频率复用网络视为多智能体系统,并使用过时的反馈CSI序列作为系统状态。通过建立有效的奖励表达式,智能体可以跟踪时变时延水声信道的变化特性并做出相应的资源分配决策。为了进一步提高智能体的决策准确度,同时避免状态空间维度增大时的部分学习成本,结合动态状态长度方法改进了MADQN-RA。仿真结果表明,所提方法实现的系统性能优于基于其他学习的方法和基于信道预测的方法,且更接近理论最优值。
