在云环境下,各种闲置资源可以通过池化形成资源池,进而利用虚拟化技术将资源池中的不同资源组合以服务的形式提供给用户使用,因此需要合理而有效的机制来分配资源.针对云环境下资源的特点,将经济学和智能方法相结合,提出了一种基于双向...在云环境下,各种闲置资源可以通过池化形成资源池,进而利用虚拟化技术将资源池中的不同资源组合以服务的形式提供给用户使用,因此需要合理而有效的机制来分配资源.针对云环境下资源的特点,将经济学和智能方法相结合,提出了一种基于双向组合拍卖的智能资源分配机制.在该机制中,提出了基于体验质量(quality of experience,简称QoE)的威望系统,引入威望衰减系数和用户信誉度,降低拍卖中恶意行为造成的影响,为资源交易提供QoE支持.对拍卖中的竞价决策,综合考虑多种因素,提出了基于BP神经网络的竞标价格决策机制,不仅可以合理确定竞标价,而且使价格可以动态适应市场变化.最后,由于组合拍卖胜标确定问题是NP完全的,因此引入群搜索优化算法,以市场盈余和总体威望为优化目标,得到资源分配方案.仿真研究结果表明,该机制是可行和有效的.展开更多
Considering the nonlinea r, time-varying and ripple coupling properties in the hydraulic servo system, a two-stage Radial Basis Function （RBF） neural network model is proposed to realize the failure detection and fa...Considering the nonlinea r, time-varying and ripple coupling properties in the hydraulic servo system, a two-stage Radial Basis Function （RBF） neural network model is proposed to realize the failure detection and fault localization. The first-stage RBF neural network is adopted as a failure observer to realize the failure detection. The trained RBF observer, working concurrently with the actual system, accepts the input voltage signal to the servo valve and the measurements of the ram displacements, rebuilds the system states, and estimates accurately the output of the system. By comparing the estimated outputs with the actual measurements, the residual signal is generated and then analyzed to report the occurrence of faults. The second-stage RBF neural network can locate the fault occurring through the residual and net parameters of the first-stage RBF observer. Considering the slow convergence speed of the K-means clustering algorithm, an improved K-means clustering algorithm and a self-adaptive adjustment algorithm of learning rate arc presented, which obtain the optimum learning rate by adjusting self-adaptive factor to guarantee the stability of the process and to quicken the convergence. The experimental results demonstrate that the two-stage RBF neural network model is effective in detecting and localizing the failure of the hydraulic position servo system.展开更多
文摘在云环境下,各种闲置资源可以通过池化形成资源池,进而利用虚拟化技术将资源池中的不同资源组合以服务的形式提供给用户使用,因此需要合理而有效的机制来分配资源.针对云环境下资源的特点,将经济学和智能方法相结合,提出了一种基于双向组合拍卖的智能资源分配机制.在该机制中,提出了基于体验质量(quality of experience,简称QoE)的威望系统,引入威望衰减系数和用户信誉度,降低拍卖中恶意行为造成的影响,为资源交易提供QoE支持.对拍卖中的竞价决策,综合考虑多种因素,提出了基于BP神经网络的竞标价格决策机制,不仅可以合理确定竞标价,而且使价格可以动态适应市场变化.最后,由于组合拍卖胜标确定问题是NP完全的,因此引入群搜索优化算法,以市场盈余和总体威望为优化目标,得到资源分配方案.仿真研究结果表明,该机制是可行和有效的.
文摘Considering the nonlinea r, time-varying and ripple coupling properties in the hydraulic servo system, a two-stage Radial Basis Function （RBF） neural network model is proposed to realize the failure detection and fault localization. The first-stage RBF neural network is adopted as a failure observer to realize the failure detection. The trained RBF observer, working concurrently with the actual system, accepts the input voltage signal to the servo valve and the measurements of the ram displacements, rebuilds the system states, and estimates accurately the output of the system. By comparing the estimated outputs with the actual measurements, the residual signal is generated and then analyzed to report the occurrence of faults. The second-stage RBF neural network can locate the fault occurring through the residual and net parameters of the first-stage RBF observer. Considering the slow convergence speed of the K-means clustering algorithm, an improved K-means clustering algorithm and a self-adaptive adjustment algorithm of learning rate arc presented, which obtain the optimum learning rate by adjusting self-adaptive factor to guarantee the stability of the process and to quicken the convergence. The experimental results demonstrate that the two-stage RBF neural network model is effective in detecting and localizing the failure of the hydraulic position servo system.
