新疆棉花产量人工神经网络预测研究

本文运用人工神经网络预测方法,建立新疆棉花产量预测模型,通过与多元线性回归预测误差的比较,说明神经网络预测方法在新疆棉花产量预测上是有效的。     

基于遗传神经网络的报价决策模型研究

为了加快建筑企业在决定投标后的报价速度,提高报价决策的成功率,利用人工智能技术的遗传算法和神经网络,建立一个报价决策模型,并以从大量成功中标的工程实测中得来的数据作为样本对模型进行了检验,结论是:在决策的准确率、推理速度以及决策系统的自学习性方面都取得了优于传统人工神经网络方法的性能,可以为建筑企业投标报价提供参考依据.     

基于Logit模型上市公司财务预警实证研究

本文针对目前企业财务危机预警模型存在的问题,提出基于Logit模型的上市公司财务预警方法.并选取60家上市公司作为样本,筛选若干个常用的财务指标,经过严格的实证数据测试,建立上市公司财务预警模型,运用该模型对公司进行预测,结果表明所设计的预警模式是有效的;同时,将人工神经网络中的BP神经网络工具运用于构建上市公司财务危机预警模型,进行对比研究.     

铝电解企业财务风险评价方法及应用

近几年,随着我国经济的快速增长,市场竞争日益激烈,铝电解企业在面临巨大的发展机遇的同时,也存在着经营不确定性。在国家节能减排政策下,技术落后、产能低下、高能耗的小型铝电解企业被淘汰,但保留下来的大型铝电解企业在新经济形势下所面临的风险比其它任何时期都要复杂。市场竞争使铝电解企业不得不面临能源危机所带来的原材料价格上涨和产品价格下跌的双重影响,财务风险管理始终贯穿于公司经营过程当中。因此,对铝电解企业财务风险进行分析、评价,建立铝电解企业财务风险评价体系是一项紧迫而现实的任务。本文以焦作万方铝业公司为例,试通过构建公司财务风险评价体系,探讨铝电解公司财务风险的评价方法。     

基于神经网络的农产品物流需求预测研究

农产品物流不仅具有一般性物流的共同特点,而且具有独特性和复杂性,这导致利用一般方法进行农产品物流需求预测不仅难度大,而且精度差。为克服单项物流需求预测的局限性及农产品物流数据的不完整性,本文利用神经网络理论,建立基于BP神经网络的农产品物流预测模型,并利用BP神经网络在学习过程中具有工作信号正向传播和误差信号反向传播的特点,通过权值的不断修正,使网络的实际输出向量更加接近期望的输出值,从而极大程度提高了物流预测的准确性。经农产品物流预测实例分析,验证了基于BP神经网络模型建立的物流需求预测模型的准确性与科学性。从而印证了人工神经网络在物流领域中的应用具有更加广泛的发展潜力。     

在土地评价中应用人工神经网络专家系统的理论与实践

利用人工神经网络模型构建的土地适宜性评价专家系统克服了传统的基于规则的专家系统的许多缺陷,具有自组织性和自适宜性。针对ＢＰ模型的训练时间过长,无法自动确定拓扑关系,难以对结果进行解释等缺陷提出了新想法和解决方案     

基于SOM的福建省城市可持续发展水平分类

基于人工神经网络(ANN)中自组织特征映射网络(SOM)的聚类功能,利用主成分分析法(PCA)提取城市社会、经济发展的多项指标,应用MATLAB 6.1软件的神经网络工具箱对福建省23个城市的可持续发展水平进行分类判定,得出分为6类的最终结果与实际情况基本相符;指出SOM网络可以避免传统聚类方法的不足,凭借其强大的学习功能,可较好地应用于城市发展的相关研究。     

基于BP神经网络的我国农产品市场风险预警研究

本文从影响农产品供给与需求的因素,以及自然、经济、政策与国际环境等影响农产品价格的因素等方面出发构建了我国农产品市场风险预警的指标体系,并以大豆为例,利用BP神经网络对大豆市场风险预警进行了实证分析,网络训练和验证结果表明BP神经网络很好地拟和、预测出了我国大豆市场风险水平,说明基于BP人工神经网络的农产品市场风险预警模型是切实可行的。     

基于进化神经网络的电力变压器故障诊断

提出了基于进化神经网络（ENNs）的电力变压器故障诊断方法。基于文中所提算法,ENNs能自动调整神经网络的网络参数以达到最佳模型。应用进化算法的全局搜索能力和神经网络的高度非线性映射性质,ENNs可以识别变压器油中溶解气体含量和相应故障类型之间的复杂关系。文中所提的ENNs以某电力公司的诊断记录为依据进行测试,并与模糊诊断系统、人工神经网络和传统方法进行对比。测试结果证明ENNs比现有的方法更能准确诊断故障,所需学习时间更少。     

土地利用结构优化的灰色线性规划方法研究——河南省三门峡市的实证分析

针对土地利用结构优化问题,将三门峡市作为研究区域,以2006年为基期年,利用灰色GM(1,1)预测模型对三门峡市规划年的人口、耕地、粮食产量、粮食单产进行预测;在此基础上,建立灰色线性规划模型对三门峡市土地利用结构进行优化,然后建立BP人工神经网络模型,通过土地利用结构方案优选评价指标体系的建立,选出三门峡市土地利用结构优化的最优方案。     

区域耕地数量变化预测方法的对比研究

研究目的:对区域耕地数量变化预测方法进行对比分析,科学合理确定区域耕地数量。研究方法:通过Pearson相关分析法筛选影响耕地变化的关键因子,利用改进的BP神经网络算法、灰色模型和多元线性回归模型法对费县耕地数量变化进行预测。研究结果:改进的BP神经网络算法相对传统的灰色模型和多元线性回归模型等预测方法具备较高的预测精度。研究结论:改进的BP神经网络算法是进行耕地数量变化预测的较好方法,具有实际指导意义,其预测结果可以为当地相关部门合理地确定耕地保有量、推进耕地保护提供科学依据。     

A hybrid commodity price‐forecasting model applied to the sugar–alcohol sector

Accurate price forecasting for agricultural commodities can have significant decision‐making implications for suppliers, especially those of biofuels, where the agriculture and energy sectors intersect. Environmental pressures and high oil prices affect demand for biofuels and have reignited the discussion about effects on food prices. Suppliers in the sugar–alcohol sector need to decide the ideal proportion of ethanol and sugar to optimise their financial strategy. Prices can be affected by exogenous factors, such as exchange rates and interest rates, as well as non‐observable variables like the convenience yield, which is related to supply shortages. The literature generally uses two approaches: artificial neural networks (ANNs), which are recognised as being in the forefront of exogenous‐variable analysis, and stochastic models such as the Kalman filter, which is able to account for non‐observable variables. This article proposes a hybrid model for forecasting the prices of agricultural commodities that is built upon both approaches and is applied to forecast the price of sugar. The Kalman filter considers the structure of the stochastic process that describes the evolution of prices. Neural networks allow variables that can impact asset prices in an indirect, nonlinear way, what cannot be incorporated easily into traditional econometric models.     

Modelling property values in Nigeria using artificial neural network

Unreliable and inaccurate property valuation has been associated with techniques currently used in property valuation. A possible explanation for these findings may be due to the utilisation of traditional valuation methods. In the current study, an artificial neural network (ANN) is applied in property valuation using the Lagos metropolis property market as a representative case. Property sales transactions data (11 property attributes and property value) were collected from registered real estate firms operating in Lagos, Nigeria. The result shows that the ANN model possesses a good predictive ability, implying that it is suitable and reliable for property valuation. The relative importance analysis conducted on the property attributes revealed that the number of servants’ quarters is the most important attribute affecting property values. The findings suggest that the ANN model could be used as a tool by real estate stakeholders, especially valuers and researchers for property valuation.     

城市土地集约利用程度的人工神经网络判定——以深圳市为例

研究目的:探索适合城市土地集约利用综合评价的非线性定量方法。研究方法:人工神经网络(ANN)模型及实证分析。研究结果:深圳市土地集约利用ANN模型评价结果显示,全市土地集约利用水平基本分4类。即集约利用-效益均衡型、集约/适度利用-效益偏好型、适度/低度利用-效益不均衡型和低度利用型。研究结论:基于ANN的城市土地集约利用评价模型,在处理不同量纲指标和减少人为主观因素上具有优势,其结果不仅能反映土地集约利用程度的区域差异,也能反映各指标对土地集约利用水平的贡献     

Forecasting agricultural production using co-integration analysis

A forecasting method, easily understood and applied by administrators/policy-makers, is developed in this paper. Based on a simple, but quite realistic assumption, that production in a given region ‘pulls’ or ‘shapes’ the volume of production nationally a co-integration forecasting method is proposed in the paper. Using data from administrative regions, the empirical analysis suggests that the property of co-integration can be used to forecast agricultural production in the short-run.     

The relationship between woody biomass consumption and economic growth: Nonlinear ARDL and causality

In this paper, the structure of the relationship between woody biomass energy consumption and real per Capita GDP was analyzed in the period of 1980–2012 for the selected African countries by ARDL (Autoregressive Distributed Lag), nonlinear ARDL, Granger causality and forecast error variance decomposition methods. After a symmetric relationship between woody biomass energy consumption and economic growth was determined by the nonlinear ARDL (NARDL) model, ARDL and Granger Causality methods were applied. According to results of the Granger Causality method, there is a unidirectional causality running from economic growth to woody biomass energy consumption for Botswana, Cameroon, Uganda, and Zambia and from woody biomass energy consumption to economic growth for Burkina Faso, Malawi, Central African Republic, Namibia, Côte d’Ivoire, Djibouti, Gabon and Zimbabwe. The bidirectional causality was supported for Kenya, Lesotho, Madagascar and Togo. Lastly, forecast error variance decomposition method was applied to support the results obtained from Granger Causality method. The forecast error variance decomposition results of real per Capita GDP and woody biomass energy consumption showed that woody biomass energy and real per Capita GDP made the important contribution to the forecast error variance of itself and each other.     

基于小波神经网络的鱼类价格预测研究

鱼类市场价格是影响渔民收益的重要因素,因此如何能够准确分析和预测鱼类的价格是一个十分重要的问题.论文在考虑了BP网络原形的一些缺点和不足,尝试在传统BP算法的基础上将网络神经元中的激励函数换成小波子函数,组建成的小波神经网络.通过对鲈鱼价格的预测,验证了小波神经网络的可行性,然后基于阿里巴巴的鲈鱼价格的历史数据,验证了该方法的合理性.最后开展对新疆乌伦古湖水产综合基地三类主要鱼类价格预测的实证研究,研究结果表明河鲈和梭鲈的价格会有小幅的波动,但高白鲑的价格会有大幅的提升.因此,小波神经网络作为一种传统神经网络的改进,可用于预测鱼类价格的短期预测,为水产基地的决策提供依据.     

基于风险分析的缙云县耕地需求量预测研究

研究目的：从粮食安全角度,结合风险分析理念,进行耕地需求量预测,为制定政策提供依据。研究方法：运用蒙特卡罗模拟方法,以县域为例,分析人均粮食消费量、出生率、死亡率、粮食耕地单产、粮经比、复种指数、粮食自给率等因素的可能概率分布,对耕地需求量进行仿真预测。研究结果：得到具有一定可信度的耕地需求量风险区间值,其平均值与常规预测方法的结果接近,但比常规预测得到更多的信息。研究结论：通过风险分析得到的耕地需求量风险区间值比单一特定的值更具有政策意义,分析耕地需求量风险区间值,可协助分析相关政策措施的有效性,有利于土地利用规划的弹性管理。     

林业生产要素配置效率研究综述及展望

在对近几年林业生产要素配置效率相关研究文献进行梳理的基础上,试图追溯林业生产要素配置效率的研究发展脉络,从生产要素配置效率的内涵及方法、林业生产要素配置效率的测算研究、林业生产低效配置的原因、提升林业生产要素配置效率的解决路径等方面的研究进行评述,并提出进一步研究展望。认为未来研究应该考虑林业生产独特的自然属性,应用遥感技术测算林业产出值,准确测算自然因素对林业产出的贡献值,构建能综合反映林业生产生态效益、经济效益和社会效益多功能林业发展的林业生产要素配置效率评价指标体系。     

Forecasting pseudo‐periodic seasonal patterns in agricultural prices

A seasonal model is proposed to forecast agricultural prices with pseudo‐periodic seasonal patterns, in which the length of the seasonal period does not remain the same over time. The seasonal effect at a season is defined as a function of the proportion of the seasonal period length elapsed up to that season, and the seasonal pattern is modeled by means of evolving splines to capture any dynamic process of change. Such a model is a useful tool to forecast seasonal behaviors. To illustrate the relevance of this modeling framework, the methodology is applied to weekly prices of tomatoes exported to German markets.