大学生综合素质的多层次模糊综合评价研究

本文首先提出了高校大学生综合素质的多层次模糊综合评价指标体系;接着基于模糊集理论建立了高校大学生综合素质的多层次模糊综合评价模型;实现了高校大学生综合素质的多层次模糊综合评价系统。     

应用综合评价模型评价学生综合素质

对学生的综合素质进行考评,是学校管理的一项重要工作。应用多层模糊综合评价模型对学生综合素质进行评价,反映了各因素的重要程度并减少了人为因素的干扰,给出了一种非常有效的量化评价手段,并通过一个实例计算表明该模型更加细致地反映了学生综合素质的差异。     

利用二级模糊综合评判进行大学体育教师综合素质评估

将二级模糊综合评判引入大学体育教师的综合素质评估，选取了评估指标及测度方法；随后，建立了一种开放的评估模型；最后，结合实际数据给出应用实例。实践证明了本文方法可行性。     

BP神经网络在学生素质综合测评系统中的应用

本文简述了BP神经网络的基本原理,提出了一种基于BP神经网络的学生素质综合测评模型。实验表明,该模型的辨识精度高,能正确实现对学生综合素质的测评。     

基于素质洋葱模型的高校图书馆员素质提升研究

阐述了提升图书馆员综合素质的重要意义,简述了素质模型和素质洋葱模型的概念,以"素质洋葱模型"为依托对高校图书馆员的现有情况进行了分析,以期找到合理的提升路径促进图书馆工作质量的全面提升。     

多元智能理论下大学生素质的模糊综合考核法

社会发展要求高校教育从多个角度考核评价学生,注重学生综合素质的提高和个性化的发展.根据加德纳的多元智能理论,在对大学生综合素质深入分析的基础上,建立了大学生综合素质多层次的分解评价模型,为综合评价大学生综合素质提供了一种更加系统、全面、可行的方法.     

AHP模型在高校学生综合素质评价体系中的应用研究

学生的综合素质受多种因素影响,如何建立科学的评估体系是各大学多年来一直探索的问题。经过分析各种因素建立了高校学生综合素质AHP模型,并对高校学生的综合素质进行科学地定性与定量分析,通过模型计算表明:综合指数得出的结果能对学生的优点和弱点作出客观评价,有利于学生的全面发展,对当前进行素质教育具有现实意义。     

大学生创业素质模型的构建及其评价

胜任素质研究是人力资源管理的新概念,它建立在胜任素质理论基础之上。提高大学生群体的创业素质,是培养高素质创业家、企业家的有效途径。本文在素质模型理论和创业理论指导下对大学生创业素质进行了探讨。通过对素质模型理论发展演进的理论综述,进而对大学生创业素质进行界定。通过问卷调查和数据统计分析,建立了大学生创业素质模型,重点运用层次分析法(AHP),对大学生创业素质模型指标体系赋予权重,并在此基础上通过专家咨询,运用模糊综合评价法对大学生样本的创业素质进行评价。在模糊综合评价的基础上,对大学生创业素质进行现状分析,并针对其不足之处提出了培养和提高策略,以求为高等院校创业教育提供有益参考。     

模糊综合素质测评在高校教师聘任选拔中的运用

本文运用模糊综合评判的方法对高校招聘工作中的人才素质进行评价,首先介绍了二级模糊综合评判模型的建立过程,然后结合实例具体说明模糊综合素质测评的操作过程,并分析了这种方法的优越性。     

大学生综合素质评价系统分析与设计

根据我校现有的大学生综合素质评价总体要求并结合实际应用制定了评价指标体系,然后运用模糊数学中模糊综合评判的方法,建立了模糊综合评价模型,最后用计算机实现数据收集与处理,为大学生综合素质的评价提供了一个科学的方法和手段。     

Periodic solution to Cohen–Grossberg BAM neural networks with delays on time scales

In this paper, we investigate first the existence and uniqueness of periodic solution in a general Cohen–Grossberg BAM neural networks with delays on time scales by means of contraction mapping principle. Then by using the existence result of periodic solution and constructing a Lyapunov functional, we discuss the global exponential stability of periodic solution for above neural networks. In the last section, we also give examples to demonstrate the validity of our global exponential stability result of the periodic solution for above neural networks.     

Existence and global exponential stability of a periodic solution for a discrete-time interval general BAM neural networks

In this paper, a discrete-time interval general BAM bidirectional associative memory neural networks model is considered. By employing the theory of coincidence degree and using Halanay-type inequality technique we establish new sufficient conditions ensuring the existence and global exponential stability of periodic solutions for the discrete-time interval general BAM bidirectional neural networks. The results obtained generalize and improve known results in [23]. An example is provided to show the correctness of our analysis.     

基于贝叶斯正则化BP神经网络的上市公司信用评价研究

在神经网络训练中引入贝叶斯正则化以提高模型的推广能力。通过与未使用提高推广能力方法和使用提前停止法两种情况进行比较,表明基于贝叶斯正则化的BP神经网络模型在上市公司信用评价中的推广能力有显著的提高。     

人工神经网络

本文介绍了人工神经网络的概念,阐述了神经网络的一般结构和分类,以及神经网络的训练算法及其范化能力的改进,并综述了神经网络的应用。     

GA优化神经网络思想

概述了神经网络的概念、神经网络的基本元素和BP算法的基本原理,同时在BP算法的基础上用遗传算法来优化BP算法的权值,并且改变了BP算法的传播方向。     

Passivity and robust passivity of stochastic reaction–diffusion neural networks with time-varying delays

In this paper, passivity and robust passivity for a general class of stochastic reaction–diffusion neural networks with Dirichlet boundary conditions and discrete time-varying delays are considered. With the help of inequality techniques and stochastic analysis, sufficient conditions are developed to guarantee passivity and robust passivity of the addressed neural networks. The obtained results in this study include some existing ones as special cases. A numerical example is carried out to illustrate the feasibility of the proposed theoretical criteria.     

面向对象的神经网络程序设计及其在非线性动态系统辨识中的应用

采用面向对象的程序设计方法（ＯＯＰ）,利用Ｃ＋＋中的类模板,建立了神经网络模型的抽象类Ｎｅｔｗｏｒｋ并提出了用Ｃ＋＋实现的神经网络通用程序,该程序具有良好的操作笥和可扩展性,并以其在非线性动态系统辨识的应用为例予以说明。     

基于小波网络的电力系统短期负荷预报研究

本文结合小波和神经网络方法进行电力系统短期负荷预测的通用模型和方法的研究,建立了负荷预报的小波网络模型,确定了有效的算法求解小波函数线性组合的尺度和时延参数以及神经网络的权值。     

A novel neural network to nonlinear complex-variable constrained nonconvex optimization

In this paper, a novel complex-valued neural network (CVNN) is proposed to investigate a nonlinear complex-variable nonconvex optimization problem (CVNOP) subject to general types of convex constraints, including inequality and bounded as well as equality constraints. The designed neural network is available to search the critical point set of CVNOP. In contrast with other related neural networks to complex-variable optimization problem, network herein contains fewer neurons and does not depend on exact penalty parameters. To our best knowledge, this is the first attempt to exploit the neural network to solve nonconvex complex-variable optimization problem. Furthermore, the presented network is also capable of solving convex or nonconvex real-variable optimization problem (RVNOP). Different from other existing neural networks for RVNOP, our network avoids the redundant computation of inverse matrix and relaxes some additional assumptions, comprising the objective function is bounded below over the feasible region or the objective function is coercive. Several numerical illustrations and practical results in beamforming provide the viability of the proposed network.     

Pinning impulsive stabilization for BAM reaction-diffusion neural networks with mixed delays

The exponential stabilization of BAM reaction-diffusion neural networks with mixed delays is discussed in this article. At first, a general pinning impulsive controller is introduced, in which the control functions are nonlinear and the pinning neurons are determined by reordering the state error. Next, based on the designed control protocol and the Lyapunov–Krasovskii functional approach, some novel and useful criteria, which depend on the diffusion coefficients and controlling parameters, are established to guarantee the global exponential stabilization of the considered neural networks. Finally, the effectiveness of the proposed control strategy is shown by two numerical examples.