Neural Networks,Decision Tree Induction and Discriminant Analysis: an Empirical Comparison

This paper presents an empirical comparison of three classification methods: neural networks, decision tree induction and linear discriminant analysis. The comparison is based on seven datasets with different characteristics, four being real, and three artificially created. Analysis of variance was used to detect any significant differences between the performance of the methods. There is also some discussion of the problems involved with using neural networks and, in particular, on overfitting of the training data. A comparison between two methods to prevent overfitting is presented: finding the most appropriate network size, and the use of an independent validation set to determine when to stop training the network.     

Applications of Artificial Neural Networks for Periodic Structures Analysis

The paper presents the nondeterministic, based on artificial neural network application approach analysis of periodic structures. We can distinguish several examples where the problem may be observed: conventional and magnetic railways, high building constructions that consist of repeatable blocks, ship and aeroplane bodies, space-shuttle periodic designs, long-beam antenna structures or mistuned blade disks with friction damping elements. The scope of research is to examine possibilities of use the neural networks for mistuning parameters definition and also to denominate its possible causes. The results obtained via neural network simulator training process are compared with the calculations based on mathematical model. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

A Comparison of Data Envelopment Analysis and Artificial Neural Networks as Tools for Assessing the Efficiency of Decision Making Units

This paper is concerned with the comparison of two popular non-parametric methodologies—data envelopment analysis and artificial neural networks—as tools for assessing performance. Data envelopment analysis has been established since 1978 as a superior alternative to traditional parametric methodologies, such as regression analysis, for assessing performance. Neural networks have recently been proposed as a method for assessing performance. In this paper, we use a simulated production technology of two inputs and one output for testing the success of the two methods for assessing efficiency. The two methods are also compared on their practical use as performance measurement tools on a set of bank branches, having multiple input and output criteria. The results demonstrate that, despite their differences, both methods offer a useful range of information regarding the assessment of performance.     

Applications of Artificial Neural Networks to combat simulations

As a result of reduced budgets and personnel levels, the Department of Defense has increased reliance on combat simulations for such diverse areas as training, testing, planning, and analysis. Each area has its own set of needs, goals, and objectives for designing future generations of combat simulation models. However, budget constraints alone mandate the development of multipleuse combat models. The bottom line is that future generations of combat models need to be faster, have higher fidelity and larger scale than current models. Research into emerging technologies for approaches to make computer simulations more effective and efficient is an essential ingredient to developing successful future generations of combat models. One emerging technology that has such potential is Artificial Neural Networks (ANN). Potential applications of ANN to combat simulation modeling are discussed. The main results of the author's dissertation Artificial Neural Network Metamodels of Stochastic Computer Simulations [1] are discussed along with the ramifications on combat modeling and recommendations for future research.     

Discriminant Analysis in Schizophrenia based on Neural Network

In this paper, we use neural network to classify schizophrenia patients and healthy control subjects. Based on 4005 high dimensions feature space consist of functional connectivity about 63 schizophrenic patients and 57 healthy control as the original data, attempting to try different dimensionality reduction methods, different neural network model to find the optimal classification model. The results show that using the Mann-Whitney U test to select the more discrimination features as input and using Elman neural network model for classification to get the best results, can reach a highest accuracy of 94.17%, with the sensitivity being 92.06% and the specificity being 96.49%. For the best classification neural network model, we identified 34 consensus functional connectivities that exhibit high discriminative power in classification, which includes 26 brain regions, particularly in the thalamus regions corresponding to the maximum number of functional connectivity edges, followed by the cingulate gyrus and frontal region.     

人工神经网络在SARS疫情分析与预测中的应用

讨论人工神经网络在 SARS疫情分析与预测中的应用 .采用三层结构的反向传播网络 ( Backpropagation network,简称 BP网络 ) ,对 SARS在中国的传播与流行趋势及控制策略建立了网络模型 .并利用实际数据拟合参数 ,针对北京、山西的疫情进行了计算仿真 .结果表明 ,该网络模型算法收敛速度较快 ,预测精度很高     

Harmonic Analysis of Neural Networks

It is known that superpositions of ridge functions (single hidden-layer feedforward neural networks) may give good approximations to certain kinds of multivariate functions. It remains unclear, however, how to effectively obtain such approximations. In this paper, we use ideas from harmonic analysis to attack this question. We introduce a special admissibility condition for neural activation functions. The new condition is not satisfied by the sigmoid activation in current use by the neural networks community; instead, our condition requires that the neural activation function be oscillatory. Using an admissible neuron we construct linear transforms which represent quite general functionsfas a superposition of ridge functions. We develop

• • • a continuous transform which satisfies a Parseval-like relation;
• • • a discrete transform which satisfies frame bounds.

Both transforms representfin a stable and effective way. The discrete transform is more challenging to construct and involves an interesting new discretization of time–frequency–direction space in order to obtain frame bounds for functions inL²(A) whereAis a compact set of Rⁿ. Ideas underlying these representations are related to Littlewood–Paley theory, wavelet analysis, and group representation theory.     

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.     

判别分析在教学法比较中的应用研究

采用平衡随机分组设计对课堂提问教学法和满堂灌法进行对比试验，判别分析表明，前者相对于后者，对促进学生的总成绩和学习综合能力都具有显著的优越性。     

Some New Approaches to the Construction and Learning of Artificial Neural Networks

We propose two new approaches to the construction of artificial neural networks: a Fourier neural network and a binary neural network. A distinctive feature of the approaches proposed is the availability of simple, linear, single-step learning algorithms, which enables one to apply them to the construction of networks with a fairly large number of neurons and weights.     

非对称细胞神经网络的稳定性分析

This paper describes the problem of stability for one-dimensional Cellular Neural Networks(CNNs). A sufficient condition is presented to ensure complete stability for a class of special CNN's with nonsymmetric templates, where the parameter in the output function is greater than or equal to zero. The main method is analysising the property of the equilibrium point of the CNNs system.     

时滞细胞神经网络的稳定性分析

研究具有时滞的细胞神经网络的稳定性问题，通过构造合适的Lyapunov函数及不等式分析技巧，给出了时滞细胞神经网络全局稳定的新的充分判据，这些结论推广了已知文献中的结果。     

On Fixed Points of Continuous Mappings Associated with Construction of Artificial Neural Networks

Doklady Mathematics - A general topological approach is proposed for the construction of converging artificial neural networks (ANN) by applying decision-making algorithms tuned on a sequence of...     

几类微分-代数方程的神经网络求解法

在非线性科学中，寻求微分方程的近似解析解一直是重要的研究课题和研究热点.利用人工神经网络原理，结合最优化方法，研究了几类微分-代数方程的近似解析解，包括指标1，2，3型Hessenberg方程及指标3型Euler-Lagrange方程，得到了方程近似解析解的表达式.通过与精确解或Runge-Kutta(龙格-库塔)数值计算结果对比，表明神经网络方法的结果有很高的精度.     

一类具有时滞的Cohen-Grossberg神经网络的指数稳定性

考虑一类具有时滞的Cohen-Grossberg神经网络,利用Lyapunov方法和微分不等式理论,得到了其全局指数稳定性的判别准则.该准则引入了更多的参数,更便于系统的设计与分析.     

Cohen-Grossberg神经网络的全局指数稳定性分析

本文研究了CohenGrossberg神经网络模型的指数稳定性.为避免构造Lyapunov函数的困难,我们采用广义相对Dalquist数方法来分析神经网络的稳定性.借助这一方法,我们不但得到了CohenGrossberg神经网络模型平衡解的存在性、唯一性和全局指数稳定性的新的充分条件,而且给出了神经网络的指数衰减估计.所获结论改进了已有文献的相关结果.     

人工神经网络在评定射击效率中的应用研究

将BP、RB、GRNN等人工神经网络引入火炮射击效率评定的计算中.通过实例运算,分析了各种神经网络在实际应用中各自的特点和需注意的问题,得到了有益的结论.     

A Preliminary Test in Discriminant Analysis

One of the aims of discriminant analysis is the allocation of unknown entities to populations that are knowna priori. Considerkpopulations. LetXdenote the vector of observations on an experimental unit, whose origin is uncertain. For the general parametric case, a test is proposed to verify the hypothesis thatXis coming from a new population. The case of normal population with equal variance is fully derived.     

Optimal Stochastic Distribution of CNTS in a Cantilever Polymer Microbeam Using Artificial Neural Networks

Mechanics of Composite Materials - The optimal stochastic distribution of carbon nanotubes (CNTs) in nanoreinforced polymer composite of a cantilevered microbeam is investigated. Finite-element...