Linear feedforward control of two-level quantum system by modulated external field

We propose a model of feedforward (open-loop) optical control of two-level atom in the linear form. This model allows to express the general form of solution for the atomic level populations via the arbitrary shapes of the control signal. Then we make numerical investigations of different shapes for the optical control signal.     

前馈加反馈补偿的分散鲁棒跟踪系统研究

本文用分散的前馈加反馈补偿器对线性时不变多变量系统进行了研究,给出了获得内稳定鲁棒跟踪系统的充要条件;分析了系统的灵敏性和稳定边际;提出了考虑瞬态响应、灵敏性和稳定边际的设计分散鲁棒跟踪系统的方法。     

Applications of Hybrid Monte Carlo to Bayesian Generalized Linear Models: Quasicomplete Separation and Neural Networks

Abstract

The “leapfrog” hybrid Monte Carlo algorithm is a simple and effective MCMC method for fitting Bayesian generalized linear models with canonical link. The algorithm leads to large trajectories over the posterior and a rapidly mixing Markov chain, having superior performance over conventional methods in difficult problems like logistic regression with quasicomplete separation. This method offers a very attractive solution to this common problem, providing a method for identifying datasets that are quasicomplete separated, and for identifying the covariates that are at the root of the problem. The method is also quite successful in fitting generalized linear models in which the link function is extended to include a feedforward neural network. With a large number of hidden units, however, or when the dataset becomes large, the computations required in calculating the gradient in each trajectory can become very demanding. In this case, it is best to mix the algorithm with multivariate random walk Metropolis—Hastings. However, this entails very little additional programming work.     

统一潮流控制器并联变换器虚拟惯量控制策略

针对统一潮流控制器(unified power flow controller,UPFC)在调节电力系统线路潮流过程中存在的功率不平衡引起的UPFC接入点电压、直流电容电压波动问题,构建UPFC的数学模型,设计稳压控制电流前馈,提出基于虚拟惯量的UPFC并联变换器控制策略,通过引入UPFC电容虚拟惯量,在并联变换器电压外环的基础上设计UPFC电容虚拟惯量控制环,以电网实际频率为输入量,降低潮流控制过程中的电压波动;为加快UPFC直流侧电容电压控制速度,将前馈补偿点进行了改进,在并联变换器dq轴有功无功分量控制环路中加入改进的前馈量,改善功率协调控制效果。搭建UPFC仿真模型,仿真结果表明,相比常规控制策略,该控制策略既保证了线路有功无功独立调节的功能,又有效地减少了电压的波动,提高了系统的响应速度。     

QSAR Modeling of Large Heterogeneous Sets of Molecules

Abstract

In aquatic toxicology, QSAR models are generally designed for chemicals presenting the same mode of toxic action. Their proper use provides good simulation results. Problems arise when the mechanism of toxicity of a chemical is not clearly identified. Indeed, in that case, the inappropriate application of a specific QSAR model can lead to a dramatic error in the toxicity estimation. With the advent of powerful computers and easy access to them, and the introduction of soft modeling and artificial intelligence in SAR and QSAR, radically different models, designed from large non-congeneric sets of chemicals have been proposed. Some of these new QSAR models are reviewed and their originality, advantages, and limitations are stressed.     

Test Problem Generator by Neural Network for Algorithms that Try Solving Nonlinear Programming Problems Globally

A test problem generator, by means of neural networks nonlinear function approximation capability, is given in this paper which provides test problems, with many predetermined local minima and a global minimum, to evaluate nonlinear programming algorithms that are designed to solve the problem globally.     

Evaluation of fuzzy regression models by fuzzy neural network

In this paper, a novel hybrid method based on fuzzy neural network for approximate fuzzy coefficients (parameters) of fuzzy linear and nonlinear regression models with fuzzy output and crisp inputs, is presented. Here a neural network is considered as a part of a large field called neural computing or soft computing. Moreover, in order to find the approximate parameters, a simple algorithm from the cost function of the fuzzy neural network is proposed. Finally, we illustrate our approach by some numerical examples.     

四层前向神经网络作为通用逼近器的一致性分析

如果转移函数σ:R→R是Tauber-Wiener函数,即σ∈(TW), 本文给出了四层前向神经网络∑pi=1ui(q∑j=1uijσ(Aij·x+θij))作为通用逼近器的一致性分析,且选取了该网络具有某类特殊性质的连结权.例如,若f,g是连续函数,且f≤g,则相应的连结权关于f,g是递增的,等等.最后为验证结论,给出了一个模拟例子.     

三级倒立摆的T-S型前馈补偿模糊神经网络控制

为解决T akag i-Sugeno型模糊神经网络在控制多变量系统时的规则组合爆炸问题,提出一种误差前馈补偿的模糊神经网络控制方案,有效实现了三级倒立摆的稳定控制。该控制方案适用对状态变量可按性质和重要程度划分的多变量系统的控制,大大减少了模糊神经网络控制器的规则数,有利于利用专家的控制经验,具有良好的鲁棒性和非线性适应能力。