Improved Kinetic Rate Constants for Nylon 6, Nylon 6,6, and Nylon 6/6,6 Copolymer

Nylon 6 and 6,6 literature data are collected over a wide range of water concentrations and temperatures (0 ≤ [W]₀ ≤ 40.8 wt%, 200 ≤ T ≤300 °C) and used to fit parameters in an updated batch reactor model. The resulting copolymerization model uses side reactions to account for the complex influence of water on kinetics and reaction equilibria. The proposed parameter estimates result in a significant improvement in the fit to the data, corresponding to a 73% reduction in the weighted‐least‐squares objective function compared to when the parameters of Arai et al. are used. Copolymerization simulations are conducted at industrially relevant conditions, shedding light on the complex influence of water and on the potential to include waste nylon 6 cyclic dimer in the feedstock. The model and parameter estimates will be helpful in future models of nylon 6/6,6 copolymerization in continuous reactor systems.     

A self-optimizing inverse analysis method for estimation of cyclic elasto-plasticity model parameters

In this paper, a novel inverse analysis methodology call a Self-Optimizing Inverse Method (Self-OPTIM) has been presented, which inversely estimates cyclic elasto-plastic constitutive model parameters using global forces and displacement on the same partial boundaries and full-(or partial-) field displacement data. A novelty of the methodology is that it automatically self-estimates material parameters by updating “full-field” reference stresses and strains through two parallel nonlinear finite element simulations. Although a well-known classical cyclic plasticity model is chosen in this paper, it must be emphasized that the proposed Self-OPTIM method is a model-independent method, which means that any advanced model can be naturally integrated with the proposed methodology. Thus, using numerically generated test data of low-carbon steel specimens (AISI 1010), the proposed Self-OPTIM method has been verified showing its successful performance to estimate nonlinear isotropic and kinematic hardening parameters, yield stress, Young’s modulus and Poisson ratio. The effects of experimental noises from CCD camera and measurement errors of the boundary forces are also investigated for the Self-OPTIM method.     

Implicitly-weighted total least squares

In a total least squares (TLS) problem, we estimate an optimal set of model parameters X, so that (A-ΔA)X=B-ΔB, where A is the model matrix, B is the observed data, and ΔA and ΔB are corresponding corrections. When B is a single vector, Rao (1997) and Paige and Strakoš (2002) suggested formulating standard least squares problems, for which ΔA=0, and data least squares problems, for which ΔB=0, as weighted and scaled TLS problems. In this work we define an implicitly-weighted TLS formulation (ITLS) that reparameterizes these formulations to make computation easier. We derive asymptotic properties of the estimates as the number of rows in the problem approaches infinity, handling the rank-deficient case as well. We discuss the role of the ratio between the variances of errors in A and B in choosing an appropriate parameter in ITLS. We also propose methods for computing the family of solutions efficiently and for choosing the appropriate solution if the ratio of variances is unknown. We provide experimental results on the usefulness of the ITLS family of solutions.     

反向热传导问题的一种新的正则化方法

讨论一个高维反向热传导问题,这是一个经典的严重不适定问题.关于这一问题我们给出一种新的正则化方法-改进的Tikhonov正则化方法,以恢复解对数据的连续依赖性.通过构造一个重要的不等式和提高先验光滑条件,获得正则解在0相似文献   

基于半参数多元Copula-GARCH模型的开放式基金投资组合风险分析

利用Copula技术对我国开放式基金市场的投资组合进行了风险分析。为克服传统Copula模型对金融尾部数据刻画能力的不足,建立了半参数的多元Copula-GARCH模型,灵活地对各支基金的边缘分布进行拟合,刻画了开放式基金投资组合的相依结构。并利用基于Copula技术的蒙特卡洛模拟,对投资组合进行了VaR分析,结果证实了所建立模型的可行性和有效性。     

Gumbel分布的简单贝叶斯估计

在实际应用中,两参数Gumbel分布的贝叶斯估计往往需要预先知道Gumbel参数的二维联合先验分布。由于获取先验分布的主观性和统计推断的复杂性,目前有关Gumbel分布贝叶斯估计理论及其性质的讨论还比较少,更不要说获得较为简单的Gumbel分布的贝叶斯估计。本文基于Kaminskiy和Vasiliy提出的简单贝叶斯估计过程,利用可靠度函数估计的区间形式表示先验信息,从而得到两个参数Gumbel分布的简单贝叶斯估计。基于此先验信息,该估计过程构造了Gumbel参数的连续联合先验分布,给出了在给定任意时点的可靠度(或累积密度)及其标准差的后验估计,为可靠性与风险评估中简单快速的使用贝叶斯估计刻画极端事件提供了可能.     

Finite-time boundedness and L2-gain analysis for switched delay systems with norm-bounded disturbance

Finite-time boundedness and finite-time weighted L₂-gain for a class of switched delay systems with time-varying exogenous disturbances are studied. Based on the average dwell-time technique, sufficient conditions which guarantee the switched linear system with time-delay is finite-time bounded and has finite-time weighted L₂-gain are given. These conditions are delay-dependent and are given in terms of linear matrix inequalities. Detail proofs are given by using multiple Lyapunov-like functions. An example is employed to verify the efficiency of the proposed method.     

An adaptive network based fuzzy inference system–auto regression–analysis of variance algorithm for improvement of oil consumption estimation and policy making: The cases of Canada,United Kingdom,and South Korea

This paper presents an adaptive network based fuzzy inference system (ANFIS)–auto regression (AR)–analysis of variance (ANOVA) algorithm to improve oil consumption estimation and policy making. ANFIS algorithm is developed by different data preprocessing methods and the efficiency of ANFIS is examined against auto regression (AR) in Canada, United Kingdom and South Korea. For this purpose, mean absolute percentage error (MAPE) is used to show the efficiency of ANFIS. The algorithm for calculating ANFIS performance is based on its closed and open simulation abilities. Moreover, it is concluded that ANFIS provides better results than AR in Canada, United Kingdom and South Korea. This is unlike previous expectations that auto regression always provides better estimation for oil consumption estimation. In addition, ANOVA is used to identify policy making strategies with respect to oil consumption. This is the first study that introduces an integrated ANFIS–AR–ANOVA algorithm with preprocessing and post processing modules for improvement of oil consumption estimation in industrialized countries.     

半参数回归模型小波估计的弱收敛速度

本文研究了误差为鞅差序列情形下的半参数回归模型.利用小波方法,在相当一般的条件下,得到了参数、非参数估计量的弱收敛速度.     

A novel scheme for the design of backstepping control for a class of nonlinear systems

A novel scheme is proposed for the design of backstepping control for a class of state-feedback nonlinear systems. In the design, the unknown nonlinear functions are approximated by the neural networks (NNs) identification models. The Lyapunov function of every subsystem consists of the tracking error and the estimation errors of NN weight parameters. The adaptive gains are dynamically determined in a structural way instead of keeping them constants, which can guarantee system stability and parameter estimation convergence. When the modeling errors are available, the indirect backstepping control is proposed, which can guarantee the functional approximation error will converge to a rather small neighborhood of the minimax functional approximation error. When the modeling errors are not available, the direct backstepping control is proposed, where only the tracking error is necessary. The simulation results show the effectiveness of the proposed schemes.