基于AM-MCMC的BP神经网络在煤矿地下水评价中的应用

BP神经网络模型用于水质进行评价的研究已经很多,然而,传统的BP神经网络无法考虑相邻水质级别临界处的模型性,评价指标较多时运行速度慢,且训练样本少、代表性差,评价结果精度不高。为此,建立了基于AM-MCMC算法的BP模型。利用AM-MCMC算法模拟足够的代表性好的样本以为BP网络训练所需,用于灌区的水质评价。实例研究表明,与传统的BP网络相比,基于AM-MCMC的BP评价结果与实际水质比较更为客观、合理。基于AM-MCMC的BP模型能考虑相邻水质级别临界处的模糊性,克服训练样本少的缺点生成足够的代表性好的样本,快速有效地对灌区水质进行评价。此外,基于AM-MCMC的BP模型还可用于洪灾损失评价、地震灾害评价及其他评价问题,具有广泛的实用性。     

贝叶斯网络结构学习研究

针对贝叶斯网络结构学习方法难以兼顾高准确率和高效率的问题,提出了一种基于Markov Chain Monte Carlo(MCMC)方法的贝叶斯网络结构学习方法的改进.改进包括:使用依赖关系分析,利用统计学的方法对采样空间进行大幅缩减,能够在精确控制准确度的情况下大幅提高时间效率;结合先验知识,从理论角度将先验知识融入评分中得到完全服从后验分布的结果;搜索最优子结构,对于特定的一些结构搜索最优子结构而不是采用贪心的方法,提高了贝叶斯网络结构学习的准确率.通过理论分析可以证明时间复杂度得到了大幅的降低.并且可以在牺牲可预知的准确率的情况下,将指数时间复杂度降为线性时间.大量的数据实验表明,经改进后的方法在时间和准确性上都具有良好的表现.     

孤岛运行方式下微电网储能系统能量成型控制策略

在分析微电网储能系统(ESS)结构的基础上,建立了微电网ESS的端口受控哈密顿(PCH)模型,设计了PCH控制器,确定了期望平衡点,给出了微电网ESS的能量成型控制策略。建立含可再生能源发电单元和ESS的微电网仿真模型对所提策略进行仿真,仿真结果表明,与典型PI闭环线性控制策略相比,所提控制策略在孤岛运行期间能很好地满足对ESS的控制要求,且具有更好的快速性、鲁棒性。     

MCMC-based local parametric sensitivity estimations

Bayesian inferences for complex models need to be made by approximation techniques, mainly by Markov chain Monte Carlo (MCMC) methods. For these models, sensitivity analysis is a difficult task. A novel computationally low-cost approach to estimate local parametric sensitivities in Bayesian models is proposed. This method allows to estimate the sensitivity measures and their errors with the same random sample that has been generated to estimate the quantity of interest. Conditions to allow a derivative-integral interchange in the operator of interest are required. Two illustrative examples have been considered to show how sensitivity computations with respect to the prior distribution and the loss function are easily obtained in practice.     

An original approximate method for estimating the invariant probability distribution of a large class of multi-dimensional nonlinear stochastic oscillators

This paper proposes an original method for obtaining analytical approximations of the invariant probability density function of multi-dimensional Hamiltonian dissipative dynamic systems under Gaussian white noise excitations, with linear non-conservative parts and nonlinear conservative parts. The method is based on an exact result and a heuristic argument. Its pertinence is attested by numerical tests.     

Bayesian selection of threshold autoregressive models

Abstract.  An approach to Bayesian model selection in self-exciting threshold autoregressive (SETAR) models is developed within a reversible jump Markov chain Monte Carlo (RJMCMC) framework. Our approach is examined via a simulation study and analysis of the Zurich monthly sunspots series. We find that the method converges rapidly to the optimal model, whilst efficiently exploring suboptimal models to quantify model uncertainty. A key finding is that the parsimony of the model selected is influenced by the specification of prior information, which can be examined and subjected to criticism. This is a strength of the Bayesian approach, allowing physical understanding to constrain the model selection algorithm.     

Bayesian Subset Model Selection for Time Series

Abstract.  This paper considers the problem of subset model selection for time series. In general, a few lags which are not necessarily continuous, explain lag structure of a time-series model. Using the reversible jump Markov chain technique, the paper develops a fully Bayesian solution for the problem. The method is illustrated using the self-exciting threshold autoregressive (SETAR), bilinear and AR models. The Canadian lynx data, the Wolfe's sunspot numbers and Series A of Box and Jenkins (1976) are analysed in detail.     

A study on the nonlinear normal mode vibration using adelphic integral

Nonlinear normal mode (NNM) vibration, in a nonlinear dual mass Hamiltonian system, which has 6^th order homogeneous polynomial as a nonlinear term, is studied in this paper. The existence, bifurcation, and the orbital stability of periodic motions are to be studied in the phase space. In order to find the analytic expression of the invariant curves in the Poincare Map, which is a mapping of a phase trajectory onto 2 dimensional surface in 4 dimensional phase space, Whittaker’s Adelphic Integral, instead of the direct integration of the equations of motion or the Birkhoff-Gustavson (B-G) canonical transformation, is derived for small value of energy. It is revealed that the integral of motion by Adelphic Integral is essentially consistent with the one obtained from the B-G transformation method. The resulting expression of the invariant curves can be used for analyzing the behavior of NNM vibration in the Poincare Map.     

Comment on ‘Persistent inputs and the standard H2-multivariable control problem’ by K. Park and J.J. Bongiorno Jr

It is necessary to welcome the publication of the paper by Park and Bongiorno Jr (Park, K. and Bongiorno Jr, J.J. (2009 Park, K and Bongiorno Jr, JJ. 2009. Persistent Inputs and the Standard H₂-multivariable Control Problem. International Journal of Control, 82: 2002–2012. [Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]), ‘Persistent Inputs and the Standard H₂-multivariable Control Problem’, International Journal of Control, 82, 2002–2012) in which the special cases in H₂-optimisation problems are considered. However, for correct orientation of readers we would like to note some publications which, in our opinion, are connected to a problem given by Park and Bongiorno Jr (2009 Park, K and Bongiorno Jr, JJ. 2009. Persistent Inputs and the Standard H₂-multivariable Control Problem. International Journal of Control, 82: 2002–2012. [Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]), but are absent there.