中国城市竞争力评价量化模型研究

本文建立一种将主、客观指标赋权方法优点相结合的城市竞争力综合评价分层构权主成分分析模型,在构建中国城市竞争力综合评价指标体系的基础上,基于该模型方法给出31个中国主要城市的城市竞争力的评价实例。并以并以判别分析方法对相关评价结果进行校验,实证分析的结果表明:这是一种适合于城市竞争力定量评价、稳定性较好且切实有效的方法模型。     

Neural networks: A biased overview

An overview of recent activity in the field of neural networks is presented. The long-range aim of this research is to understand how the brain works. First some of the problems are stated and terminology defined; then an attempt is made to explain why physicists are drawn to the field, and their main potential contribution. In particular, in recent years some interesting models have been introduced by physicists. A small subset of these models is described, with particular emphasis on those that are analytically soluble. Finally a brief review of the history and recent developments of single- and multilayer perceptrons is given, bringing the situation up to date regarding the central immediate problem of the field: search for a learning algorithm that has an associated convergence theorem.     

Signal Penetration into Thick Clouds Studied by Multi-Layer Data Observed with a Micro-Pulse Lidar

Lidar return signals are studied for a micro-pulse lidar under sky conditions with multi-layer clouds. From theoretical considerations on the lidar signal-to-noise ratio, it is estimated that the maximum cloud optical thickness detectable is about 3.7. This result reasonably agrees with the actual multi-layer cloud data obtained from observations in Sukhothai, Thailand. Deviations from theoretical prediction, however, are found for a geometrically thin but dense cloud, and for a moderately concentrated but geometrically thick cloud. The effect of multiple scattering is also discussed.     

A tutorial on support vector machine-based methods for classification problems in chemometrics

This tutorial provides a concise overview of support vector machines and different closely related techniques for pattern classification. The tutorial starts with the formulation of support vector machines for classification. The method of least squares support vector machines is explained. Approaches to retrieve a probabilistic interpretation are covered and it is explained how the binary classification techniques can be extended to multi-class methods. Kernel logistic regression, which is closely related to iteratively weighted least squares support vector machines, is discussed. Different practical aspects of these methods are addressed: the issue of feature selection, parameter tuning, unbalanced data sets, model evaluation and statistical comparison. The different concepts are illustrated on three real-life applications in the field of metabolomics, genetics and proteomics.     

Well-posedness and stability for an elliptic-parabolic free boundary problem modeling the growth of multi-layer tumors

In this paper we study a free boundary problem modeling the growth of multi-layer tumors. This free boundary problem contains one parabolic equation and one elliptic equation, defined on an unbounded domain in R2 of the form 0 〈 y 〈p（x,t）, where p（x,t） is an unknown function. Unlike previous works on this tumor model where unknown functions are assumed to be periodic and only elliptic equations are evolved in the model, in this paper we consider the case where unknown functions are not periodic functions and both elliptic and parabolic equations appear in the model. It turns out that this problem is more difficult to analyze rigorously. We first prove that this problem is locally well-posed in little H61der spaces. Next we investigate asymptotic behavior of the solution. By using the principle of linearized stability, we prove that if the surface tension coefficient y is larger than a threshold value y〉0, then the unique flat equilibrium is asymptotically stable provided that the constant c representing the ratio between the nutrient diffusion time and the tumor-cell doubling time is sufficiently small.     

On the non-classical mathematical models of coupled problems of thermo-elasticity for multi-layer shallow shells with initial imperfections

Mathematical modeling of evolutionary states of non-homogeneous multi-layer shallow shells with orthotropic initial imperfections belongs to one of the most important and necessary steps while constructing numerous technical devices, as well as aviation and ship structural members. In first part of the paper fundamental hypotheses are formulated which allow us to derive Hamilton–Ostrogradsky equations. The latter yield equations governing shell behavior within the applied hypotheses and modified Pelekh–Sheremetev conditions. The aim of second part of the paper is to formulate fundamental hypotheses in order to construct coupled boundary problems of thermo-elasticity which are used in non-classical mathematical models for multi-layer shallow shells with initial imperfections. In addition, a coupled problem for multi-layer shell taking into account a 3D heat transfer equation is formulated. Third part of the paper introduces necessary phase spaces for the second boundary value problem for evolutionary equations, defining the coupled problem of thermo-elasticity for a simply supported shallow shell. The theorem regarding uniqueness of the mentioned boundary value problem is proved. It is also proved that the approximate solution regarding the second boundary value problem defining condition for the thermo-mechanical evolution for rectangular shallow homogeneous and isotropic shells can be found using the Bubnov–Galerkin method.     

Frozen 1-RSB structure of the symmetric Ising perceptron

We prove, under an assumption on the critical points of a real-valued function, that the symmetric Ising perceptron exhibits the ‘frozen 1-RSB’ structure conjectured by Krauth and Mézard in the physics literature; that is, typical solutions of the model lie in clusters of vanishing entropy density. Moreover, we prove this in a very strong form conjectured by Huang, Wong, and Kabashima: a typical solution of the model is isolated with high probability and the Hamming distance to all other solutions is linear in the dimension. The frozen 1-RSB scenario is part of a recent and intriguing explanation of the performance of learning algorithms by Baldassi, Ingrosso, Lucibello, Saglietti, and Zecchina. We prove this structural result by comparing the symmetric Ising perceptron model to a planted model and proving a comparison result between the two models. Our main technical tool towards this comparison is an inductive argument for the concentration of the logarithm of number of solutions in the model.     

竖向载荷作用下框架结构力学模型的简化与计算

针对结构力学与工程结构设计中力学模型和载荷作用方式的简化,分别采用一次性加载法、模拟施工三加载法和分层法,研究了竖向载荷作用下框架结构力学模型的简化与计算,分析了三种方法的计算假定和计算结果,给出了合理适用范围。一次性加载法更适用于抗连续性倒塌分析和建筑结构改造设计计算,模拟施工三加载法更适用于新建工程的设计计算,分层法作为一种简化的手算方法可作为理解结构力学原理的基本方法。     

多层介质膜脉宽压缩光栅的严格矢量分析

用于展宽和压缩激光脉冲的多层膜脉宽压缩光栅是由多层介质高反膜和位于其顶层的浮雕光栅构成。以设计的高反射多层膜为基础,利用傅里叶模式理论分析了其衍射场分布,给出了TE波自准直角入射的使用条件下,多层介质膜脉宽压缩光栅衍射效率的表达式。以-1级衍射效率为评价函数,分别讨论了HfO2和SiO2为顶层材料时,多层膜脉宽压缩光栅-1级衍射效率高于0.95的光栅结构参量范围。结果表明,在该条件下,选择HfO2为顶层材料时,光栅结构参量有较大的取值范围。给出了优化的光栅结构参量,并分析了光栅制作误差及其使用条件的宽容度,对光栅制作工艺和使用具有一定的指导意义。     

Training Multilayer Perceptron with Genetic Algorithms and Particle Swarm Optimization for Modeling Stock Price Index Prediction

Predicting stock market (SM) trends is an issue of great interest among researchers, investors and traders since the successful prediction of SMs’ direction may promise various benefits. Because of the fairly nonlinear nature of the historical data, accurate estimation of the SM direction is a rather challenging issue. The aim of this study is to present a novel machine learning (ML) model to forecast the movement of the Borsa Istanbul (BIST) 100 index. Modeling was performed by multilayer perceptron–genetic algorithms (MLP–GA) and multilayer perceptron–particle swarm optimization (MLP–PSO) in two scenarios considering Tanh (x) and the default Gaussian function as the output function. The historical financial time series data utilized in this research is from 1996 to 2020, consisting of nine technical indicators. Results are assessed using Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and correlation coefficient values to compare the accuracy and performance of the developed models. Based on the results, the involvement of the Tanh (x) as the output function, improved the accuracy of models compared with the default Gaussian function, significantly. MLP–PSO with population size 125, followed by MLP–GA with population size 50, provided higher accuracy for testing, reporting RMSE of 0.732583 and 0.733063, MAPE of 28.16%, 29.09% and correlation coefficient of 0.694 and 0.695, respectively. According to the results, using the hybrid ML method could successfully improve the prediction accuracy.