BP神经网络在上海住宅市场需求预测中的应用

人工神经网络是近期发展最快的人工智能领域研究成果之一 ,本文在介绍 BP神经网络的有关原理的基础上 ,建立了一个上海住宅市场的 BP神经网络模型 ,并通过该模型对上海住宅市场的需求进行了预测和分析 .分析结果表明人工神经网络方法在住宅市场需求预测中的应用是可行的并且是有效的 .     

期货市场中持仓量及参与者对资产价格的影响研究

本文通过建立一个期货市场的均衡模型,提出在具有套保需求和有限风险承受能力的前提下,期货价格能够预测未来资产价格变动的方向,持仓量能够辅助预测未来资产价格变动的剧烈程度;此外,市场中不知情投机者具有风险调整市场收益的作用,不知情套保者的参与能够稳定市场。对于持仓量是否能够辅助预测未来资产价格变动的剧烈程度,本文利用中国商品期货市场数据进行了实证检验,结果表明与理论研究的结论一致。     

我国棕榈油期货市场效率研究——基于VAR及其扩展模型的实证分析

利用2007年10月至2012年3月国内棕榈油期货市场、国际棕榈油产区以及国内棕榈油、豆油、菜籽油现货市场的交易日价格,运用VAR及其扩展模型验证了关于期货市场效率的两个假说.研究结果表明:棕榈油国际产区市场对来自期货市场的冲击有一定程度的反映,但不是主要作用,且期货市场对国际产区市场还没有预测力;棕榈油期货市场价格对现货市场发现功能不强,但对棕榈油、菜籽油、豆油现货价格具有较强的预测力,长期均衡关系对现货市场的调整力小.     

我国期货市场违约风险的研究

引入违约距离的概念,建立了期货市场违约风险评估模型,采用GARCH-M模型对期货合约价格收益的波动率进行估计.运用此模型研究了郑州商品交易所上市品种小麦的违约风险,所得结果与实际市场结果相吻合.因此,可以运用本文提出的期货市场违约风险评估模型能预测临近交割月时期货市场发生违约的概率,实时捕捉期货市场发生违约事件的信息.     

股指期货市场波动率的预测研究

准确刻画和预测股指期货市场的波动率,有利于实现股指期货的价格发现、套期保值和市场调节等功能.文章首先在已实现极差异质性自回归模型(the Heterogeneous AutoRegressive with Realized Range,HAR-RRV model)的基础上,构建HAR-HLT和LHAR-HLT模型;接着,以中国股市中沪深300股指期货的5分钟高频交易数据为样本,通过运用HARHLT和LHAR-HLT模型分析股指期货市场波动率的特征以及预测股指期货市场未来的波动率.研究发现:在HAR-HLT和LHAR-HLT模型中,高频已实现极差、低频已实现极差和趋势已实现极差都包含较多对未来波动率的预测信息;股指期货市场的波动率存在短期的"动量效应"和中长期的"反转效应",其杠杆效应并不明显;HAR-HLT和LHAR-HLT模型对未来1日、1周和1月波动率样本外预测能力都明显强于目前常用的HAR-RRV类模型.     

BP网络和RBF网络在期货预测中的比较研究

通过仿真实例,对BP网络和RBF网络在期货预测应用上的表现性能进行了比较研究,仿真结果表明,BP网络更适合于期货市场价格预测.实际的期货预测应用中,此结论可指导神经网络模型的选择.     

基于人工神经网络的商业银行信用风险模型

在对人工神经网络的基本原理进行简要介绍的基础上 ,着重对构建商业银行信用风险的人工神经网络模型进行了研究 ,实证结果表明 ,人工神经网模型具有很高的预测精度     

基于支持向量回归的豆油期货数据分析

随着期货市场在经济中的影响逐渐增强,对期货市场的研究具有实际意义.选取豆油期货指数进行研究,首先将数据进行归一化处理,然后选用支持向量回归方法建模分析.分析结果表明该方法对短期内的数据预测具有较好的效果,对了解期货市场的近期走势提供一些借鉴.     

中国黄金期货市场波动特征及模型预测研究

选取上海期货交易所黄金期货价格指数日内10分钟高频收益数据,构造了经调整的已实现极差波动率估计序列,利用6类GARCH模型建模分析,描述了黄金期货价格指数的波动特征.运用多种损失函数比较了GARCH类模型样本外波动率预测精度的优劣,并在此基础上,采用一种渐进正态分布检验法评估了GARCH类模型的预测效果.结果显示,黄金期货已实现极差波动率估计序列具有尖峰厚尾、集聚性、持续性等特征.对于黄金期货市场,ACD-GARCH模型具有相对最好的波动率预测能力.     

FANN模型在金属矿冲击地压预测中的应用

金属矿山冲击地压是采矿工程中所涉及到的重大灾害之一.冲击地压的发生是由多方面因素造成的,具有动态和模糊性,在客观上属于一个复杂的非线性动力学过程,这使得冲击地压预测系统的数据处理不能按照常规的线性系统法进行处理.根据模糊神经网络理论,本文给出了多源信息融合的模糊人工神经网络(FANN)模型.通过具体工程应用,说明了本文分析模型的正确性和有效性.     

三层前向人工神经网络全局最优逼近

提出了求解不等式约束非线性优化问题的群体复合形进化算法 ,提出的算法能充分利用目标函数值的信息、优化搜索过程具有较强的方向性和目标性 ,收敛速度较快 ,且是全局优化算法 ;将群体复合形进化算法应用于三层前向人工神经网络逼近 ,提出了三层前向人工神经网络全局最优逼近算法 ;将三层前向人工神经网络全局最优逼近算法应用于实例 ,表明了提出的全局最优逼近算法的有效性 .     

神经元网络在SAS中的实现(Ⅰ)

大多数常见的人工神经元网络—多层感知器（ＭＬＰ），无非是非线性回归以及判别模型。其训练方法通常是适合大型并行机硬件实现的梯度下降算法。对于一般的计算环境，ＳＡＳ系统中若干优化算法远比神经元网络的并行算法更有效。本文解释神经元网络是什么，描述神经元网络与统计模型的关系，介绍神经元网络在ＳＡＳ中的实现。     

The use of cascade-correlation neural networks in University fund raising

In recent years, many Colleges and Universities in the USA have been facing a serious financial crisis since many state governments have reduced their support for higher education. There is no doubt that one of the solutions to this crisis depends on the successful implementation of University fund raising programs. Identifying the potential donors is an important part of this process. The objective of this research was to develop a cascade-correlation neural network to predict the types of people who would most likely be potential donors. A comparison of the classification accuracy between neural networks and multiple discriminant analyses (MDA) was also conducted. Our results indicated that neural networks could perform as well as MDA in overall accuracy. Furthermore, neural networks could predict with a lot more accuracy the actual donor (Type I hit) than MDA. Our study is the first published case study on the use of artificial neural networks for University fund raising.     

Neural network as a simulation metamodel in economic analysis of risky projects

An artificial neural network (ANN) model for economic analysis of risky projects is presented in this paper. Outputs of conventional simulation models are used as neural network training inputs. The neural network model is then used to predict the potential returns from an investment project having stochastic parameters. The nondeterministic aspects of the project include the initial investment, the magnitude of the rate of return, and the investment period. Backpropagation method is used in the neural network modeling. Sigmoid and hyperbolic tangent functions are used in the learning aspect of the system. Analysis of the outputs of the neural network model indicates that more predictive capability can be achieved by coupling conventional simulation with neural network approaches. The trained network was able to predict simulation output based on the input values with very good accuracy for conditions not in its training set. This allowed an analysis of the future performance of the investment project without having to run additional expensive and time-consuming simulation experiments.     

Neural network modeling of hydrological systems: A review of implementation techniques

This paper presents a review of procedural steps and implementation techniques used in the development of artificial intelligence models, generally referred to as artificial neural networks (ANNs), within the water resources domain. It focusses on identifying different areas wherein ANNs have found application thereby elucidating its advantages and disadvantages as well as various challenges encountered in its use. Results from this review provide useful insights into how the performance of ANNs can be improved and potential areas of application that are yet to be explored in hydrological modeling. Recommendations for Resource Managers

• Development of integrated and hybrid artificial intelligent tools is critical to achieving improved forecasts in hydrological modeling studies.

• Further research into comprehending the internal mechanisms of neural networks is required to obtain a practical meaning of each network component deployed to solve real‐world problems.

• More robust optimization techniques and tools like differential evolution, particle swarm optimization and deep neural nets, are yet to be fully explored in the water resources analysis, and should be given more attention to enhance neural networks aptitude for modeling complex and nonlinear hydrological processes.

     

Robust neural modeling for the cross-sectional analysis of accounting information

The performance of robust artificial neural network models in learning bivariate relationships between accounting magnitudes is assessed in this paper. Predictive performances of a number of modeling paradigms (namely, linear models, log-linear structures, classical ratios and artificial neural networks) are compared with regard to the problem of modeling a number of the most outstanding accounting ratio relations. We conduct a large scale analysis, carried out on a representative Spanish data base.     

Effort estimates through project complexity

This paper reports the results obtained from use of project complexity parameters in modeling effort estimates. It highlights the attention that complexity has recently received in the project management area. After considering that traditional knowledge has consistently proved to be prone to failure when put into practice on actual projects, the paper endorses the belief that there is a need for more open-minded and novel approaches to project management. With a view to providing some insight into the opportunities that integrate complexity concepts into model building offers, we extend the work previously undertaken on the complexity dimension in project management. We do so analyzing the results obtained with classical linear models and artificial neural networks when complexity is considered as another managerial parameter. For that purpose, we have used the International Software Benchmarking Standards Group data set. The results obtained proved the benefits of integrating the complexity of the projects at hand into the models. They also addressed the need of a complex system, such as artificial neural networks, to capture the fine nuances of the complex systems to be modeled, the projects.     

A hybrid spares demand forecasting method dedicated to mining industry

The paper addresses the problem of lumpy demand forecasting which is typical for spare parts. Several prediction methods are presented in the paper - traditional techniques based on time series and advanced methods which use artificial neural networks. The paper presents a new hybrid spares demand forecasting method dedicated to mining companies. The method combines information criteria, regression modeling and artificial neural networks. The paper also discusses simulation research related to efficiency assessment of the chosen variable selection methods and its application in the newly developed forecasting method. The assessment of this method is conducted by a comparison with traditional methods and is based on selected forecast errors.     

Solvable models of layered neural networks based on their differential structure

We introduce the notion of solvable models of artificial neural networks, based on the theory of ordinary differential equations. It is shown that a solvable, three layer, neural network can be realized as a solution of an ordinary differential equation. Several neural networks in standard use are shown to be solvable. This leads to a new, two-step, non-recursive learning paradigm: estimate the differential equation which the target function satisfies approximately, and then approximate the target function in the solution space of that differential equation. It is shown experimentally that the proposed algorithm is useful for analyzing the generalization problem in artificial neural networks. Connections with wavelet analysis are also pointed out.     

改进的CATSMLP预警藻类污染

人工神经网络由于其很多的特点与优势现已被广泛关注和运用.但是人工神经网络也存在学习过程易陷入局部极小、易出现震荡和网络存在冗余连接或节点等缺陷.针对这些不足,一种新的级联M LP神经网络CATSM LP比ATSM LP有更好的鲁棒性和高度的解释性,并且是一个万能逼近器.采用粒子群优化算法对其进行优化使其鲁棒性增强,具有更快速的收敛能力和更好的寻优能力,从而能更有效的建模.藻类的生长是湖泊等水体污染程度的一个直接表现形式.在某些情况下,甚至精密仪器都不能测出某些藻类污染物,因此需要好的方法越来越受到专家学者们的重视.将其用于藻类污染预警,仿真试验表明其用于环境污染防治有很好的效果,值得推广应用.