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This paper discusses issues related to the approximation capability of neural networks in modeling and control. We show that neural networks are universal models and universal controllers for a class of nonlinear dynamic systems. That is, for a given dynamic system, there exists a neural network which can model the system to any degree of accuracy over time. Moreover, if the system to be controlled is stabilized by a continuous controller, then there exists a neural network which can approximate the controller such that the system controlled by the neural network is also stabilized with a given bound of output error. 相似文献
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本文将移动Agent技术和分布式网络管理技术应用于保障特种作战的无线网络中,提出了一种基于移动A-gent的新型无线网络管理模型。该模型具有良好的可伸缩性,具备一定的抗干扰和抗毁能力。根据此模型构建了诸军兵种特种联合作战概念仿真系统,为移动Agent技术应用于特种作战网络环境以及其他战场环境中的可行性和优越性提供了定性定量分析的依据。 相似文献
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针对多指灵巧手钢缆传动系统的非线性,提出一种基于分散神经网络的位置控制方法.通过
对复杂的钢缆传动系统施加不同的输入可以得到特定的相对简单的输入输出数据,利用这种
特定的输入输出数据学习传动系统的非线性关系得到多个分散的神经网络,再根据传动系统
的结构特性用分散的神经网络求取钢缆传动系统的逆模型,用于直接逆控制,从而达到补偿
非线性误差的目的.同时应用在线神经网络的适时补偿使系统长时间保持良好的运行状态.
实验证明这种方法可大大提高位置跟踪精度,取得比较满意的结果. 相似文献
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基于BP神经网络的智能控制器设计及其应用 总被引:3,自引:0,他引:3
张铮 《计算机工程与应用》2005,41(13):204-206
神经网络可以被用来计算复杂的输入与输出结果集之间的关系,因此具有强大的控制能力。论文以工业洗衣机为控制对象,建立了BP神经网络智能控制模型,提出了BP网络的一种离线式学习方法,并在微机中实现。通过实际应用检验,其通用性强、容错性好。 相似文献
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Yong Liao 《Computer Communications》2011,34(13):1549-1558
Network virtualization provides the ability to run multiple concurrent virtual networks over a shared substrate. However, it is challenging to design such a platform to host multiple heterogenous and often highly customized virtual networks. Not only high degree of flexibility is desired for virtual networks to customize their functions, fast packet forwarding is also required. This paper presents PdP, a flexible network virtualization platform capable of achieving high speed packet forwarding. A PdP node has multiple machines to perform packet processing for virtual networks hosted in the system. To forward packets in high speed, the data plane of a virtual network in PdP can be allocated with multiple forwarding machines to process packets in parallel. Furthermore, a virtual network in PdP can be fully customized. Both the control plane and data plane of a virtual network run in virtual machines so as to be isolated from other virtual networks. We have built a proof-of-concept prototyping PdP platform using off-the-shelf commodity hardware and open source software. The performance evaluation results show that our system can closely match the best-known packet forwarding speed of software router running in commodity hardware. 相似文献
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基于Elman网络的非线性系统神经元自适应预测控制 总被引:5,自引:0,他引:5
提出在非线性系统的E1man网络辨识模型的基础上,用单神经元设计预测控制器的方案。Elman网络在BP网络的基础上,加入反馈信号,利用内部状态反馈来描述系统的非线性动力学行为,提高了学习速度,适合于动态系统的实时辨识。神经元结构简单,且有很强的自学习和自适应能力,它根据系统的期望输出与一步超前预测输出之间的偏差,并通过某种特定的学习算法在线调整控制器的参数,使控制器能够适应对象参数的变化,从而实现对一类非线性系统的有效控制。仿真实验证明了该方案的有效性。 相似文献
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基因调控网络是一类基本且重要的生物网络,通过对其进行控制可以实现生物系统功能的调节。在生物系统中,通过外部的干预控制构造关于基因调控网络的控制理论成为了非常热门的研究主题。目前,作为一种重要的网络模型,带有干扰且上下文相关的概率布尔网络已经被广泛地应用于基因调控网络优化控制问题的研究中。针对无限范围的优化控制问题,文中提出了一种基于概率模型检测和遗传算法的近似最优控制策略的计算方法。首先,该方法将无限范围控制中定义的期望总成本归约为离散时间马尔科夫链上的平稳状态回报;然后,构建包含固定控制策略的带有干扰且上下文相关的概率布尔网络模型,采用带回报属性的时序逻辑公式表示固定控制策略的成本,采用概率模型检测器PRISM进行自动计算。进一步,采用遗传算法,将固定控制策略编码为遗传算法解空间中的个体,基于其控制成本,定义个体的适应度值,将PRISM作为求解器,通过在解空间上迭代地执行遗传操作获取近似最优解。将所提方法应用于WNT5A网络中,实验结果证明了该方法的有效性。 相似文献
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传统的端到端的拥塞控制机制不适应主动网络,针对主动网络面临的拥塞问题,提出了一种自适应的主动网络拥塞控制解决方案.在中间节点为转发到相邻节点的主动信包建立缓冲队列,以缓冲区中队列长度来表明节点的拥塞程度,通过对前向节点计算单元进行控制来改变当前节点拥塞状况,网络中相关节点通过协作对网络进行拥塞控制.理论分析和模拟试验结果表明,不管网络初始状态如何,该方案均能使各节点迅速达到动态平衡,快速消除主动网络拥塞. 相似文献
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Xinjin Liu 《International journal of systems science》2013,44(10):1950-1957
In this article, stability analysis and decentralised control problems are studied for a special class of linear dynamical networks. Necessary and sufficient conditions for stability and stabilisability under a decentralised control strategy are given for this type of linear networks. Especially, two types of linear regular networks, star-shaped networks and globally coupled networks, are studied in detail, respectively. A dynamical network can be viewed as a large-scale system composed of some subsystems with some coupling structures, based on this, the relationship between the stability of a network and the stability of its corresponding subsystems is studied. Different from the discussions that the subsystems in networks vary with different coupling structures (Duan, Z.S., Wang, J.Z., Chen, G.R., and Huang, L. (2008), ‘Stability Analysis and Decentralised Control of a Class of Complex Dynamical Networks’, Automatica, 44, 1028–1035), the subsystems in network discussed in this article remain unchanged with different interconnections which is the same as in general large-scale system. It is also pointed out that some subsystems must be made unstable for the whole network to be stable in some special cases. Moreover, the controller design method based on parameter-dependent Lyapunov function is provided. 相似文献
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一种适用于无线传感器网络的功率控制MAC协议 总被引:17,自引:1,他引:17
功率控制技术通过减少节点的发射功率来降低能耗,但节点间不对称的发射功率会增加网络的冲突概率并降低吞吐量.根据实际环境中的节点部署情况,引入了基于Pareto分布的系统模型.研究了传感器网络中功率控制技术在节省能量方面的性能,提出了一种基于SMAC(sensor-MAC)可适用于无线传感器网络的功率控制MAC(media access control)协议.此协议使用功率控制调度算法选择最优相邻节点,使网络中节点的拓扑连接得到优化,在保证网络连通性的同时,降低通信的冲突率,扩大网络的吞吐量.信息的传递以最优功率发射,并使通信节点具有反作用冲突节点的能力,从而在降低网络能耗的同时保证了节点间通信的公平性.实验仿真结果显示,与现有的几种重要方案相比,新的功率控制MAC协议使网络具有了更大的有效吞吐量及更长的生存时间. 相似文献
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目前大部分访问控制机制实施于网络边缘,无法解决网络内部的安全问题。文章提出了一种基于主机的网络层访问控制机制,它由密钥协商协议和报文检测协议组成,对主机的网络行为和报文传递提供安全控制,适合在局域网等小范围网络实施,能够解决网络内部的假冒、篡改等安全问题。在局域网环境下实现了基于Linux平台的访问控制系统,对系统总体设计方案、开发平台、关键机制的实现方法及相关技术进行了详细叙述。并对实现的原型系统进行简单测试和分析。 相似文献
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基于复合正交神经网络的自适应逆控制系统 总被引:10,自引:0,他引:10
目前,在自适应逆控制系统中常采用BP神经网络,而BP网络存在算法复杂、易陷入局部极小解等不足。而正交神经网络能克服BP网络的不足,但由于正交神经网络学习算法存在某些局限性,提出了一种复合正交神经网络,该正交网络结构与三层前向正交网络相同,不同的是正交网络的隐单元处理函数采用带参数的Sigmoid函数的复合正交函数,该神经网络算法简单,学习收敛速度快,并能对网络的函数参数进行优化,为非线性系统的动态建模提供了一种方法。仿真实验表明,网络在用于过程的自适应逆控制中具有很高的控制精度和自适应学习能力。该动态神经网络比其它神经网络具有更强的建模能力与学习适应性,有线性、非线性逼近精度高等优异特性,非常适合于实时控制系统。 相似文献
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Shengfeng Zhou Mou Chen Chong-Jin Ong Peter C. Y. Chen 《Neural computing & applications》2016,27(5):1317-1325
In this paper, an adaptive neural network (NN) tracking controller is developed for a class of uncertain multi-input multi-output (MIMO) nonlinear systems with input saturation. Radial basis function neural networks are utilized to approximate the unknown nonlinear functions in the MIMO system. A novel auxiliary system is developed to compensate the effects induced by input saturation (in both magnitude and rate) during tracking control. Endowed with a switching structure that integrates two existing representative auxiliary system designs, this novel auxiliary system improves control performance by preserving their advantages. It provides a comprehensive design structure in which parameters can be adjusted to meet the required control performance. The auxiliary system signal is utilized in both the control law and the neural network weight-update laws. The performance of the resultant closed-loop system is analyzed, and the bound of the transient error is established. Numerical simulations are presented to demonstrate the effectiveness of the proposed adaptive neural network control. 相似文献
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A systematic approach has been developed to construct neural networks for qualitative analysis and reasoning. These neural networks are used as specialized parallel distributed processors for solving constraint satisfaction problems. A typical application of such a neural network is to determine a reasonable change of a system after one or more of its variables are changed. A six-node neural network is developed to represent fundamental qualitative relations. A larger neural network can be constructed hierarchically for a system to be modeled by using six-node neural networks as building blocks. The complexity of the neural network building process is thus kept manageable. An example of developing a neural network reasoning model for a transistor equivalent circuit is demonstrated. The use of this neural network model in the equivalent circuit parameter extraction process is also described 相似文献