A novel strategy of probability density function （PDF） shape control is proposed in stochastic systems. The control er is designed whose parameters are optimal y obtained through the improved particle swarm optimiza...A novel strategy of probability density function （PDF） shape control is proposed in stochastic systems. The control er is designed whose parameters are optimal y obtained through the improved particle swarm optimization algorithm. The parameters of the control er are viewed as the space position of a particle in particle swarm optimization algorithm and updated continual y until the control er makes the PDF of the state variable as close as possible to the expected PDF. The proposed PDF shape control technique is compared with the equivalent linearization technique through simulation experiments. The results show the superiority and the effectiveness of the proposed method. The control er is excellent in making the state PDF fol ow the expected PDF and has the very smal error between the state PDF and the expected PDF, solving the control problem of the PDF shape in stochastic systems effectively.展开更多
Traditionally, it is widely accepted that measurement error usually obeys the normal distribution. However, in this paper a new idea is proposed that the error in digitized data which is a major derived data source in...Traditionally, it is widely accepted that measurement error usually obeys the normal distribution. However, in this paper a new idea is proposed that the error in digitized data which is a major derived data source in GIS does not obey the normal distribution but the p-norm distribution with a determinate parameter. Assuming that the error is random and has the same statistical properties, the probability density function of the normal distribution, Laplace distribution and p-norm distribution are derived based on the arithmetic mean axiom, median axiom and p-median axiom, which means that the normal distribution is only one of these distributions but not the least one. Based on this ideal distribution fitness tests such as Skewness and Kurtosis coefficient test, Pearson chi-square chi(2) test and Kolmogorov test for digitized data are conducted. The results show that the error in map digitization obeys the p-norm distribution whose parameter is close to 1.60. A least p-norm estimation and the least square estimation of digitized data are further analyzed, showing that the least p-norm adjustment is better than the least square adjustment for digitized data processing in GIS.展开更多
作为电力系统中的基本量测设备,电子式电压互感器(electronic voltage transformers,EVTs)的测量精度对系统的监控、控制与安全运行至关重要。为此,提出了一种基于混合深度模型和自适应窗宽概率密度估计的互感器测量误差区间预测模型。...作为电力系统中的基本量测设备,电子式电压互感器(electronic voltage transformers,EVTs)的测量精度对系统的监控、控制与安全运行至关重要。为此,提出了一种基于混合深度模型和自适应窗宽概率密度估计的互感器测量误差区间预测模型。首先,通过改进的集合经验模态分解对历史比差特征进行数据前处理。其次,提出了基于数据驱动的双向时序卷积网络、双向门控循环单元和多头注意力机制混合深度学习模型,对分解后的不同模态分量进行预测。此外,引入自适应选择最优窗宽的核密度概率估计方法,拟合预测结果构建不同置信度下的预测区间,并比较不同核函数对于预测区间的影响。通过算例分析,验证了所提模型在提高确定性预测和概率区间预测准确度方面的有效性。展开更多
针对随机激励下振动系统的减振问题,提出了考虑摩擦与非线性阻尼的混联Ⅱ型惯容非线性能量阱(nonlinear energy sink,简称NES),建立了含新型NES主系统的动力学控制方程。首先,采用蒙特卡洛数值方法,研究了非线性刚度对减振性能的影响,...针对随机激励下振动系统的减振问题,提出了考虑摩擦与非线性阻尼的混联Ⅱ型惯容非线性能量阱(nonlinear energy sink,简称NES),建立了含新型NES主系统的动力学控制方程。首先,采用蒙特卡洛数值方法,研究了非线性刚度对减振性能的影响,当非线性刚度比κ_(21)逐渐增大时,主结构和混联Ⅱ型惯容NES的位移概率密度函数出现了双峰变为单峰,以及速度概率密度函数由单峰变为双峰的随机跳跃现象。主结构的位移概率密度函数对非线性刚度κ_(22)的敏感性比κ_(21)更高,κ_(22)最佳取值区间为200~1 000。其次,研究了噪声强度、阻尼比和惯质比对减振性能的影响,当噪声强度小于0.1或惯质比μ在0.1左右时,惯容NES的减振效果较好。虽然线性阻尼比λ_(1)和非线性阻尼比λ_(21)、λ_(22)增大会导致主结构和混联Ⅱ型惯容NES的概率密度函数出现分岔不稳定现象,但增大非线性阻尼比有助于改善惯容NES的减振性能。最后,采用差分进化法对惯容NES的参数进行了优化。本研究可为受随机激励的振动系统减振研究提供技术参考。展开更多
基金supported by the National Natural Science Fundation of China(61273127)the Specialized Research Fund of the Doctoral Program in Higher Education(20106118110009+2 种基金20116118110008)the Scientific Research Plan Projects of Shaanxi Education Department(12JK0524)the Young Teachers Scientific Research Fund of Xi’an University of Posts and Telecommunications(1100434)
文摘A novel strategy of probability density function （PDF） shape control is proposed in stochastic systems. The control er is designed whose parameters are optimal y obtained through the improved particle swarm optimization algorithm. The parameters of the control er are viewed as the space position of a particle in particle swarm optimization algorithm and updated continual y until the control er makes the PDF of the state variable as close as possible to the expected PDF. The proposed PDF shape control technique is compared with the equivalent linearization technique through simulation experiments. The results show the superiority and the effectiveness of the proposed method. The control er is excellent in making the state PDF fol ow the expected PDF and has the very smal error between the state PDF and the expected PDF, solving the control problem of the PDF shape in stochastic systems effectively.
文摘Traditionally, it is widely accepted that measurement error usually obeys the normal distribution. However, in this paper a new idea is proposed that the error in digitized data which is a major derived data source in GIS does not obey the normal distribution but the p-norm distribution with a determinate parameter. Assuming that the error is random and has the same statistical properties, the probability density function of the normal distribution, Laplace distribution and p-norm distribution are derived based on the arithmetic mean axiom, median axiom and p-median axiom, which means that the normal distribution is only one of these distributions but not the least one. Based on this ideal distribution fitness tests such as Skewness and Kurtosis coefficient test, Pearson chi-square chi(2) test and Kolmogorov test for digitized data are conducted. The results show that the error in map digitization obeys the p-norm distribution whose parameter is close to 1.60. A least p-norm estimation and the least square estimation of digitized data are further analyzed, showing that the least p-norm adjustment is better than the least square adjustment for digitized data processing in GIS.
文摘作为电力系统中的基本量测设备,电子式电压互感器(electronic voltage transformers,EVTs)的测量精度对系统的监控、控制与安全运行至关重要。为此,提出了一种基于混合深度模型和自适应窗宽概率密度估计的互感器测量误差区间预测模型。首先,通过改进的集合经验模态分解对历史比差特征进行数据前处理。其次,提出了基于数据驱动的双向时序卷积网络、双向门控循环单元和多头注意力机制混合深度学习模型,对分解后的不同模态分量进行预测。此外,引入自适应选择最优窗宽的核密度概率估计方法,拟合预测结果构建不同置信度下的预测区间,并比较不同核函数对于预测区间的影响。通过算例分析,验证了所提模型在提高确定性预测和概率区间预测准确度方面的有效性。
文摘针对随机激励下振动系统的减振问题,提出了考虑摩擦与非线性阻尼的混联Ⅱ型惯容非线性能量阱(nonlinear energy sink,简称NES),建立了含新型NES主系统的动力学控制方程。首先,采用蒙特卡洛数值方法,研究了非线性刚度对减振性能的影响,当非线性刚度比κ_(21)逐渐增大时,主结构和混联Ⅱ型惯容NES的位移概率密度函数出现了双峰变为单峰,以及速度概率密度函数由单峰变为双峰的随机跳跃现象。主结构的位移概率密度函数对非线性刚度κ_(22)的敏感性比κ_(21)更高,κ_(22)最佳取值区间为200~1 000。其次,研究了噪声强度、阻尼比和惯质比对减振性能的影响,当噪声强度小于0.1或惯质比μ在0.1左右时,惯容NES的减振效果较好。虽然线性阻尼比λ_(1)和非线性阻尼比λ_(21)、λ_(22)增大会导致主结构和混联Ⅱ型惯容NES的概率密度函数出现分岔不稳定现象,但增大非线性阻尼比有助于改善惯容NES的减振性能。最后,采用差分进化法对惯容NES的参数进行了优化。本研究可为受随机激励的振动系统减振研究提供技术参考。
文摘间歇过程的优化控制依赖于过程精确的数学模型,数据驱动的建模方法是目前间歇过程模型研究中的热点问题。突破传统数据驱动建模方法中采用均方差(mean squared error,MSE)作为准则函数的思想,提出一种新颖的间歇过程数据驱动建模方法,引入了概率密度函数(probability density function,PDF)控制的概念,构造间歇过程模型误差控制系统,将模型的可调参数作为控制系统的输入,模型误差PDF的形状作为控制系统的输出,从而把开环模型参数辨识问题转化为模型误差PDF形状的闭环控制问题。通过可调参数控制模型误差PDF的空间分布状态,不仅能够保障模型精度,还可控制模型误差的空间分布状态,从而消除模型中的有色噪声。仿真实验表明,基于模型误差PDF形状的间歇过程数据驱动模型具有较好的建模精度、鲁棒性和泛化能力,为间歇过程的数据驱动建模提供了一条新途径。
