中学生创造力智能评价的实现

评价学生的创造力是一项极富挑战性的工作,创造力评价是培养开发创造力的首要环节.当需要评价的被试、创造力指标个数、指标等级水平都较多时,创造力评价工作便陷入了僵局,利用人工甚至无法完成.学习矢量量化(LVQ)神经网络在模式识别方面具有良好的性能,据此尝试用LVQ神经网络进行创造力评价.根据创造力评价指标及等级的数目,构建了由输入层、隐含层、输出层组成的LVQ神经网络,用训练好的网络对测试样本进行仿真测试,仿真结果和实际情况正好相符,体现出LVQ神经网络在创造力评价中的实用和有效性.     

学习向量量化神经网络在判别分析上的应用

神经网络用于判别分析是一个新的研究课题,给出学习向量量化神经网络在判别分析上的应用.计算实例表明,用学习向量量化神经网络用于分类是准确和可靠的.     

模糊概率神经网络水质评价模型及其应用

鉴于水质类型和分级标准存在模糊性,将模糊数学中的相对隶属度理论和概率神经网络和相结合,构建了模糊概率神经网络水质评价模型(FPNN).阐明了该模型的构建方法,提出了基于指标相对隶属度矩阵插值构建学习样本的方法,并将该模型应用于实际水质评价.通过与综合评判法、属性识别法和BP网络法的比较,验证了该模型操作简便,评价结果客观可靠.     

基于熵权的可变模糊集方法在水质污染评价中的应用

水质污染评价是水环境污染防治的重要基础,针对评价过程中评价指标的不确定性,将可变模糊集理论应用于水质污染评价中,首先,建立合理的指标体系与等级标准,然后,将熵值原理引入该方法中,利用指标实测数据的波动性来计算指标权重,最后,利用模糊可变评价模型对水质污染进行组合评价,并将均值作为最终评价结果,将此方法应用于泰州市新通扬运河水质污染评价中,结果表明,与模糊综合评价相比,基于熵权的可变模糊集方法评价结果合理、客观,具有更好的可靠性与稳定性,为水质污染评价工作提供了一种新的研究方法与思路.     

改进的灰色相似关联度模型在水质评价中的应用

基于灰色系统理论,提出一种改进的灰色相似关联度模型.通过计算评价对象的单因素隶属度矩阵,得到与各标准水质级别在不同评价指标处的灰色相似关联系数,最后根据灰色相似关联系数得到对各标准水质级别的综合隶属度.将此方法应用于延河流域的八个监测断面进行水质综合评价,取得了较好的结果.在此基础上进一步分析和改进了最大隶属度原则,在考虑最大隶属度的同时,综合考虑评价对象对各标准水质级别隶属度的分布,并制定了调整规则.     

淮河水质污染的综合评价模型

根据淮河流域近一年半的12个主要观测站四项主要水质指标的检测数据,利用动态加权函数和Borda函数等方法,建立了水质污染的综合评价模型,对淮河流域的水质污染状况做出了分析评价.     

支持向量机回归方法在地表水水质评价中的应用

将支持向量机方法应用于地表水质评价问题中,建立了多指标水质综合评价的支持向量机回归模型.在地表水质评价标准的基础上采用内插法获得学习样本,经过训练,得到水质评价的分类区间;然后以实测资料对所建模型进行检验,研究结果表明,支持向量机回归模型性能良好、预测精度高、简便易行,是水质评价的一种有效方法,具有广阔的应用前景.     

基于灰色关联理论的钱塘江河口段水质评价研究

基于钱塘江河口段水质污染的系统分析,建立了水质污染评价指标体系,利用灰色关联理论构建了水质污染评价数学模型,提出了5级-6区间指标归一化方法,使得超标水质的影响能得到真实反映.根据水质实测数据,对该河段水质状况做出了综合评价,提出了防治污染,改善水质状况的对策.     

基于组合权重的模糊数学法对水质的评价

水质综合评价为水环境治理提供指导性建议,是水环境管理的重要参考.而水质各个指标权重的确定一直是研究的重点和难点.运用数学方法中的离差平方和最小原理,建立一个确定指标权重的优化模型,结合超标加权法和熵值法两种方法所确定的权重值,得到一个组合权重,并将其应用于模糊数学法对水质进行评价.以铁岭市环保监测站所得的辽河流域马仲河门脸断面2008年—2012年五年数据为基础进行实例分析,选取门脸断面中的溶解氧、COD、BOD、氨氮、总磷、石油类、挥发酚7项作为评价指标,应用上述模糊数学法进行水质等级分析,得到五年的水质为:2008、2009两年为Ⅴ类水质;2011年水质最好,达到Ⅰ类;2010年为Ⅴ类,2012年为Ⅳ类水质.并使用主成份分析法和综合指数模型两种方法进行验证,结果表明这种模型有较高的精度和可靠性.所以,模型也可为其它的权重确定方法在求解权重值时提供一定参考.     

改进交叉评价DEA在模糊综合评价中的应用

借鉴对抗型交叉评价的思想,首先利用对抗型交叉评价DEA(数据包络分析)模型对模糊综合评价的量化指标进行评价,三角模糊化后将其作为模糊综合评价量化指标的输入与非量化指标数据合成进行二次评价,以此建构了一种基于对抗型交叉评价DEA的模糊综合评价方法.方法可从根本上解决已有评价方法中模糊量化结果的不确定性问题,使客观数据与主观因素并存的多属性决策更加可靠.最后,通过算例说明了方法的应用.     

Comparative Analysis of Artificial Neural Network Models: Application in Bankruptcy Prediction

This study compares the predictive performance of three neural network methods, namely the learning vector quantization, the radial basis function, and the feedforward network that uses the conjugate gradient optimization algorithm, with the performance of the logistic regression and the backpropagation algorithm. All these methods are applied to a dataset of 139 matched-pairs of bankrupt and non-bankrupt US firms for the period 1983–1994. The results of this study indicate that the contemporary neural network methods applied in the present study provide superior results to those obtained from the logistic regression method and the backpropagation algorithm.     

基于端点白化权函数聚类的水环境质量等级评价分析

为更准确地评价水环境质量,将端点三角白化权函数灰色评估引入到水质综合评价中.针对以往灰色白化权函数评估主要通过测定指标灰聚类系数判定被评价对象的优劣强弱等级进行等级识别评价,对三角白化权函数计算方法进一步扩展,完成多评价对象的准确等级定位及比较排序.将这种方法应用于山东省聊城市东昌湖水水质评价中,经实例计算证明基于三角白化权函数灰色评估评价模型的稳定性、合理性和实用性为水质评价研究方法提供了新的思路和借鉴.     

A locally linear neuro-fuzzy model for supplier selection in cosmetics industry

Supplier selection and evaluation is a complicated and disputed issue in supply chain network management, by virtue of the variety of intellectual property of the suppliers, the several variables involved in supply demand relationship, the complex interactions and the inadequate information of suppliers. The recent literature confirms that neural networks achieve better performance than conventional methods in this area. Hence, in this paper, an effective artificial intelligence (AI) approach is presented to improve the decision making for a supply chain which is successfully utilized for long-term prediction of the performance data in cosmetics industry. A computationally efficient model known as locally linear neuro-fuzzy (LLNF) is introduced to predict the performance rating of suppliers. The proposed model is trained by a locally linear model tree (LOLIMOT) learning algorithm. To demonstrate the performance of the proposed model, three intelligent techniques, multi-layer perceptron (MLP) neural network, radial basis function (RBF) neural network and least square-support vector machine (LS-SVM) are considered. Their results are compared by using an available dataset in cosmetics industry. The computational results show that the presented model performs better than three foregoing techniques.     

Utilizing feed-back neural network approach for solving linear Fredholm integral equations system

This paper intended to offer an architecture of artificial neural networks (NNs) for finding approximate solution of a second kind linear Fredholm integral equations system. For this purpose, first we substitute the N-th truncation of the Taylor expansion for unknown functions in the origin system. By applying the suggested neural network for adjusting the real coefficients of given expansions in resulting system. The proposed NN is a two-layer feed-back neural network such that it can get a initial vector and then calculates it’s corresponding output vector. In continuance, a cost function is defined by using output vector and the target outputs. Consequently, the reported NN using a learning algorithm that based on the gradient descent method, will adjust the coefficients in given Taylor series. Eventually, we have showed this method in comparison with existing numerical methods such as trapezoidal quadrature rule provides solutions with good generalization and high accuracy. The proposed method is illustrated by several examples with computer simulations.     

Exergy as a unified measure of water quality

The concept of exergy has been applied to ecological evaluation, resource accounting and environmental impact assessment. As a suitable indicator for ecological evaluation, exergy analysis presents a unified thermodynamic measure of objective evaluation of resources and environment with exergetic unit. A case study of water quality assessment using exergy analysis is presented. Compared with other existing methods, exergy accounting provides a unitary and objective measure for water pollution as a result of the application of the thermodynamic concept to water quality assessment.     

Solving the uncapacitated multi-facility Weber problem by vector quantization and self-organizing maps

The uncapacitated multi-facility Weber problem is concerned with locating m facilities in the Euclidean plane and allocating the demands of n customers to these facilities with the minimum total transportation cost. This is a non-convex optimization problem and difficult to solve exactly. As a consequence, efficient and accurate heuristic solution procedures are needed. The problem has different types based on the distance function used to model the distance between the facilities and customers. We concentrate on the rectilinear and Euclidean problems and propose new vector quantization and self-organizing map algorithms. They incorporate the properties of the distance function to their update rules, which makes them different from the existing two neural network methods that use rather ad hoc squared Euclidean metric in their updates even though the problem is originally stated in terms of the rectilinear and Euclidean distances. Computational results on benchmark instances indicate that the new methods are better than the existing ones, both in terms of the solution quality and computation time.     

基于BP神经网络的企业信用评估模型

本文研究了企业信用评估中的模型问题.以商业企业为例,阐述了基于BP神经网络的信用评估模型的原理,通过建立指标体系,讨论基于BP神经网络的评价模型的实现,对模型的不足进行了分析,并提出改进建议.