ASYMPTOTICAL STABILITY OF NON-AUTONOMOUS DISCRETE-TIME NEURAL NETWORKS WITH GENERALIZED INPUT-OUTPUT FUNCTION

In this paper, we first introduce the model of discrete-time neural networkswith generalized input--output function and present a proof of the existence of afixed point by Schauder fixed-point principle. Secondly, we study the uniformlyasymptotical stability of equilibrium in non-autonomous discrete--time neuralnetworks and give some sufficient conditions that guarantee the stability of itby using the converse theorem of Lyapunov function. Finally, several examplesand numerical simulations are given to illustrate and reinforce our theories.     

Derivative-free optimization and neural networks for robust regression

Large outliers break down linear and nonlinear regression models. Robust regression methods allow one to filter out the outliers when building a model. By replacing the traditional least squares criterion with the least trimmed squares (LTS) criterion, in which half of data is treated as potential outliers, one can fit accurate regression models to strongly contaminated data. High-breakdown methods have become very well established in linear regression, but have started being applied for non-linear regression only recently. In this work, we examine the problem of fitting artificial neural networks (ANNs) to contaminated data using LTS criterion. We introduce a penalized LTS criterion which prevents unnecessary removal of valid data. Training of ANNs leads to a challenging non-smooth global optimization problem. We compare the efficiency of several derivative-free optimization methods in solving it, and show that our approach identifies the outliers correctly when ANNs are used for nonlinear regression.     

对称广义拟向量平衡问题

介绍了对称广义拟向量平衡问题,并且通过非线性纯量函数,利用不动点定理,证明了对称广义拟向量平衡问题解的存在性定理．     

数学建模中的数据挖掘方法

结合我校数模培训和参赛的经验，介绍了数据挖掘中的多元回归分析，主成分分析、人工神经网络等方法在建模中的一些成功应用。     

误差为相依序列时的非参数回归模型

本文对非参数多元回归模型Y~(j)(x_(in))=g(x_(in))+e~(j)(x_(in))中误差序列{e~(j)(x_(in)),j≥1}为m_0—相依序列和局部广义高斯序列时,给出未知函数g(x)的估计量的相合性。     

基于广义最大熵的顾客满意度测评研究

本文提出了应用广义最大熵（Generalized Maximum Entropy,简称 GME）的方法对顾客满意度进行测评,该方法能够处理有限的或者不完全数据.采用了一个应用于美国顾客满意指数（ACSI）的简单模型生成仿真数据,比较GME和PLS两种方法的性能.仿真结果表明,与PLS方法相比,GME方法具有更高的拟合精度和较低的预测误差,在样本数据较少时,表现更为明显.     

Existence of Equilibrium in Generalized Games with Abstract Convexity Structure

The aim of this paper is to prove the existence of equilibrium for generalized games or abstract economies in contexts where the convexity conditions on strategy spaces and preference correspondences are relaxed and an arbitrary number of agents is considered. The results are based on a fixed-point theorem in which the convexity condition on sets and images of correspondences is replaced by a general notion of abstract convexity, called mc-spaces, generalizing the notions of simplicial convexity, H-spaces, and G-convex spaces.     

ON GLOBAL ROBUST STABILITY FOR COMPETITIVE NEURAL NETWORKS WITH TIME DELAYS AND DIFFERENT TIME SCALES

In this paper, using the theory of topological degree and Liapunov functional methods, the authors study the competitive neural networks with time delays and different time scales and present some criteria of global robust stability for this neural network model.     

非参数可加模型的迭代自适应稳健变量选择

本文结合稳健损失函数、B样条逼近和自适应组Lasso研究一个高维可加模型,以识别``大p小n'下的不显著协变量.与传统的最小二乘自适应组Lasso相比,该方法具有较好的抵消重尾误差和异常值的影响. 为证明方便,本文进一步考虑了更一般的加权稳健组Lasso估计,且该权向量对所建议的估计量具有模型选择oracle性质和渐近正态性的证明中起着关键作用. 稳健组Lasso和自适应稳健组Lasso可以看作是加权稳健组Lasso在不同权向量下的特殊情况. 在实际应用中,我们使用稳健组~Lasso~获得初始估计以降低问题的维数,然后使用迭代自适应稳健组Lasso选择非零分量. 数值结果表明,所提出的方法对中等规模的样本具有良好的适用性.高维基因TRIM32数据验证了该方法的应用.     

一类具混合时滞随机神经网络稳定性的推广与应用

将一类具有混合时滞随机神经网络均方渐近稳定的判据推广到不确定神经网络的鲁棒稳定性,所导出的判据都表示为线性矩阵不等式(LMI)的形式,可通过使用一些标准的数值方法求解.最后给出了一个简单的例子说明所提出的判定条件的有效性和可应用性.     

New LMI-based criteria for global robust stability of delayed neural networks

Some novel, linear matrix inequality based, criteria for the uniqueness and global robust stability of the equilibrium point of Hopfield-type neural networks with delay are presented. A comparison of the present criteria with the previous criteria is made.     

Fuzzy Entropy： Axiomatic Definition and Neural Networks Model

The measure of uncertainty is adopted as a measure of information. The measures of fuzziness are known as fuzzy information measures. The measure of a quantity of fuzzy information gained from a fuzzy set or fuzzy system is known as fuzzy entropy. Fuzzy entropy has been focused and studied by many researchers in various fields. In this paper, firstly, the axiomatic definition of fuzzy entropy is discussed. Then, neural networks model of fuzzy entropy is proposed, based on the computing capability of neural networks. In the end, two examples are discussed to show the efficiency of the model.     

L1-optimal estimates for a regression type function in R

Let X₁, X₂, …, X_n be random vectors that take values in a compact set in R^d, d ≥ 1. Let Y₁, Y₂, …, Y_n be random variables (“the responses”) which conditionally on X₁ = x₁, …, X_n = x_n are independent with densities f(y | x_i, θ(x_i)), i = 1, …, n. Assuming that θ lives in a sup-norm compact space Θ_q,d of real valued functions, an optimal L₁-consistent estimator of θ is constructed via empirical measures. The rate of convergence of the estimator to the true parameter θ depends on Kolmogorov's entropy of Θ_q,d.     

ON GLOBAL ROBUST STABILITY FOR INTERVAL HOPFIELD NEURAL NETWORKS WITH TIME DELAY

The theorem obtained by Liao was not true (see [2]). So, this paper presents some criteria of global robust stability for interval Hopfield neural networks with time delay. The methods to judge the robust stability are practical and easily verifiable.     

一类分数阶Hopfield神经网络的鲁棒指数稳定性

本文研究了一类具有Caputo导数的分数阶Hopfield神经网络的鲁棒指数稳定性, 得到了保证其鲁棒指数稳定性的一些充分条件. 最后利用数值仿真验证了结论的正确性和有效性.     

学生学习成绩相关性的研究

从大学生学习成绩出发,应用广义线性回归模型研究了学生学年间成绩的相关性,揭示了大学一年级,尤其是一年级的二学期是影响学生后期学习的关键阶段.进一步应用聚类分析,得到学生成绩在大学一年级就已经埋下了两极分化的隐患,应用判别分析得出了两极分化的关键课程.该结论为教育教学和学生管理提供决策依据.