一种简易的无模型控制算法--改进的同时扰动随机逼近控制

同时扰动随机逼近控制算法是一种特别适合于应用在工况变化大、非线性强的工业过程的无模型控制算法。但该算法采用了神经网络作为函数逼近器，使得在工程上的实现过于复杂，文章对此作出改进，使其大大简化，更适合于在工程上应用。     

Subharmonic Resonance Behavior for the Classical Hydrogen Atomic System

Previously unexplored resonance conditions are shown to exist for the classical hydrogen atomic system, where the electron is treated as a classical charged point particle following the nonrelativistic Lorentz-Dirac equation of motion about a stationary nucleus of opposite charge. For circularly polarized (CP) light directed normal to the orbit, very pronounced subharmonic resonance behavior is shown to occur with a variety of interesting properties. In particular, only if the amplitude of the CP light exceeds a critical value, will the resonance continue without radius and energy decay. A perturbation analysis is carried out to illustrate the main features of the behavior. The present phenomena adds to a growing list of other nonlinear dynamical behaviors of this simple system, that may well be important for more deeply understanding classical and quantum connections.     

A Delay-Dependent Approach to Robust H ∞ Control for Uncertain Stochastic Systems with State and Input Delays

In this paper, the problem of delay-dependent robust H _∞ control for uncertain stochastic systems with state and input delays is investigated. The time delays are assumed to be bounded and time varying and the uncertainties are assumed to be norm bounded. By using the Lyapunov functional method, a new delay-dependent robust H _∞ control scheme is presented in terms of linear matrix inequalities (LMIs). Some numerical examples are given to illustrate the effectiveness of the proposed approach. This work is partially supported by the Natural Science Foundation of China (60674055, 60774047), and the Taishan Scholar Programme of Shandong Province. An erratum to this article can be found at     

应用CADET方法对某型反舰导弹脱靶量的统计分析

本文采用协方差描述函数技术(CADET)对某型反舰导弹制导段进行了统计分析,构造了制导段的統计分析模型,解决了CADET方法应用中的一些实际问题,提供了研究随机控制系统的实用、有效分析程序。实例计算表明:方法简便、程序可靠、结果正确。     

Towards optimal allocation of computer resources: Trade-offs between uncertainty quantification,discretization and model reduction

The computational complexity of numerical models can be broken down into contributions ranging from spatial, temporal and stochastic resolution, e.g., spatial grid resolution, time step size and number of repeated simulations dedicated to quantify uncertainty. Controlling these resolutions allows keeping the computational cost at a tractable level whilst still aiming at accurate and robust predictions. The objective of this work is to introduce a framework that optimally allocates the available computational resources in order to achieve highest accuracy associated with a given prediction goal. Our analysis is based on the idea to jointly consider the discretization errors and computational costs of all individual model dimensions (physical space, time, parameter space). This yields a cost-to-error surface which serves to aid modelers in finding an optimal allocation of the computational resources (ORA). As a pragmatic way to proceed, we propose running small cost-efficient pre-investigations in order to estimate the joint cost-to-error surface, then fit underlying complexity and error models, decide upon a computational design for the full simulation, and finally to perform the designed simulation at near-optimal costs-to-accuracy ratio. We illustrate our approach with three examples from subsurface hydrogeology and show that the computational costs can be substantially reduced when allocating computational resources wisely and in a situation-specific and task-specific manner. We conclude that the ORA depends on a multitude of parameters, assumptions and problem-specific features and, hence, ORA needs to be determined carefully prior to each investigation.     

基于不确定性用气量的输气管网供气可靠度计算方法

由于未来用气量是一个随机变量,过去采用点预测的方式无法客观体现出其随机性的特征,由此得到的供气可靠性评价结果也难以客观反映实际情况。为了准确预测用气量,在调研输气管网供气量预测模型研究进展的基础上,提出了一种采用基于小波分解的神经网络模型来预测随机性用气量的供气可靠度计算方法,通过对比分析模型预测结果与实际用气量的误差,确定用气量所服从的分布类型及参数,结合管道的最大允许输气量,以供气可靠度最高作为目标函数,建立了优化流量分配及计算管网供气可靠度的数学模型,进而利用该模型对某一虚拟管网进行了供气可靠性评价。结果表明:(1)所建模型求解的流量分配方案优先保障权重较大/较为重要的用户,但会牺牲其他用户的供气可靠度,管网总体供气可靠度亦会下降;(2)取消权重后,管网总体供气可靠度提高,优先保障距离气源地较近的用户,若不要求完全满足用户的用气量,则在降低一定的标准后,所有用户的供气可靠度都能达标。结论认为:所提出的计算方法结合了用气量随机的特性,能够更加客观地评价管网的供气可靠度,同时由于引入了用户的权重,在计算管网供气能力的时候能优先满足重要的用户,更加符合实际情况,其评价结果可以指导输气管网的高效运行。     

STABILITY ANALYSIS OF ARTIFICIAL NEURAL NETWORKS UNDER RANDOM PERTURBATION

Based on the theory of stochastic differential equation,the stability of a kind ofcontinuous-time generalized Hopfield neural networks with white noise perturbation is studiedin the paper,and the related stability criteria and design requirements of neural networks areestablished.     

Azeotrope formation in gasoline–ethanol blends. Part 1 – Impact of nonionic on E10 distillation curve

Blending 10?vol% of ethanol into hydrocarbon base gasoline (HBG) increases significantly the vapour pressure of the blend (E10), and exhibits near-azeotrope behaviour that severely affects the shape of E10 distillation curve. The distillation curves of HBG and E10 fuel blend, were constructed using ASTM D86 distillation data, and the areas under each distillation curve, were calculated through the numerical trapezoid rule (NTR) and calculus definite integration (CDI) methods. Consequently, the area due to azeotrope formation (ADAF), was estimated. In this paper, we present the impact of small concentration of nonionic surfactant on the area under distillation curve (AUDC) of E10 fuel blend and the area due to azeotrope formation (ADAF). Also, the influences of the added surfactant on the volatility criteria of the investigated E10 fuel blend were discussed.     

同位协同随机建模方法在储层预测中的应用

以大庆葡南油田葡333区块为例,采用同位协同随机建模技术,以反应储层含油性的电阻率测井参数作主变量和用地震波阻抗参数作协变量,对储集层的含油砂岩厚度进行了预测。根据对该区块储层下限的认识,提取了各个随机模拟实现的含油砂岩厚度,并以10 m含油砂岩厚度为风险门槛值分析了本区钻井风险。通过对新老钻井的钻遇情况分析表明:应用序贯高斯同位协同模拟方法,协同地震属性数据进行储集层预测的精度较高;利用多个随机模拟实现的差异性评价预测,可以有效地降低钻井风险。     

Unsupervised Bayesian image segmentation using orthogonal series

This paper deals with the problem of unsupervised image segmentation which consists in first mixture identification phase and second a Bayesian decision phase. During the mixture identification phase, the conditional probability density function (pdf) and the a priori class probabilities must be estimated. The most difficult part is the estimation of the number of pixel classes or in other words the estimation of the number of density mixture components. To resolve this problem, we propose here a Stochastic and Nonparametric Expectation-Maximization (SNEM) algorithm. The algorithm finds the most likely number of classes, their associated model parameters and generates a segmentation of the image by classifying the pixels into these classes. The non-parametric aspect comes from the use of the orthogonal series estimator. Experimental results are promising, we have obtained accurate results on a variety of real images.