基于模糊神经网络算法应用在电力系统中的研究

电力系统[1]是由发电、输电、变电、配电和用电等多层FNN的复杂的非线性控制系统组成。采用FNN弥补了神经网络在电力系统模糊数据处理方面的不足和模糊逻辑在学习方面的缺陷学科。     

基于FNN的垂直切换驻留时间自适应调整

本文通过采用FNN(模糊神经网络)优化方法,将其用在处理网络性能参数RSS值上,来自适应地产生对应的最佳驻留时间门限Tbest,并与未采用该方法的情况进行了相关仿真与对比。     

商品代销的财务处理方法

商品代销是商品所有委托另一个企业销售其商品的活动。委托另一个企业代销商品的商品所有称为“委托方”,接受代销商品的企业称为“受托方”。在商品代销业务中,受托方只是一个代理商,委托方虽然将商品交付给受托方,但并未转移该商品的所有权,如果在期末受托方仍然持有末售出的代销商品,应当归入委托方的存货中。委托方设置“委托销商品”料目用于核算交付给受托方销售的商品。受托方可设置：（1）“受托代销商品”科目核算接受委托代为销售的商品;（2）“代销商品款”科目,核算接受代销商品的价款。代销通常有视同买断和收取手续费两种方式。     

企业形象广告的策划路径

随着经济和技术的不断发展和进步,商品的差异性越来越小,趋于同质化,导致消费者在五光十色的商品面前不知所措。因而"消费者为什么买这种商品,而不是其他的商品或替代品"的问题,就成为同质化时代企业所必须思考的问题。而企业形象广告的出现,为消费者购买商品找到了一个理由。     

探讨网络营销的推荐系统及检索系统

互联网使网上销售产品的种类和规模大幅度增长,利用相关的信息检索技术,大大降低了顾客的搜索成本。信息检索系统不仅增加了顾客的消费剩余,而且商品供应也可根据商品的市场状态。推荐系统利用从顾客的行为中分析出的知识和商品的自身特点来指导顾客选择最适合自己需要的商品,为网上销售产品的企业实现增值。因此,创建一个好的推荐系统和一个个性化的信息检索系统可以增强网络销售竞争力。     

匠心多彩 业绩不凡

蓬勃发展的贵州省旅游商品产业是一个绿色工程、生态工程、阳光工程、新希望工程，更是一个富民工程。贵州省为促进这个特色产业的可持续发展，搭建了一个专业、综合、系统的旅游商品产业服务平台。     

消费者重点关注商品的评价研究

本文为平板电脑消费者重点关注的典型商品设计了一套评价方案,将商品的质量特征按客观质量与感知质量两个类别分解为多个独立的要素,然后通过调查问卷、商品采样、商品检测等环节对每一个要素进行评价,得到该商品的商品质量水平评价。该方案为商品管理部门构建消费者重点关注商品的评价体系、掌握商品消费需求与维权状况、充分发挥消费者权益保护职能、引导消费者科学消费提供了重要参考和有效支持。     

新会计准则中“发出商品”的核算要点

<正>2008年1月1日,企业将全面实行新的会计准则,对一些会计科目也做了调整,其中在旧制度中使用的"分期收款发出商品"一并在"发出商品"中统一核算。"发出商品"是一个过渡性的科目,在新准则中,核算不满足收入确认条件的已发出商品,下面,我就自己在实践运用该科目中得出的几点体会,与大家作一交流。第一,我将"发出商品"比做一个蓄水池,其借方的发生额就好像不断地蓄水,而贷方发生额则是在定期地放水,留在这个蓄水池里的水就是未确认收入的定单对应的发货成本。"发出商品"是承上启下的科目,它解决了发货与确认收入存在时间差的矛盾,但很多企业在实践中都有这样的体会,这是一个庞大的蓄水池,"发出商品"的借方余额比较大,     

发展仓储促销售 重视安全增效益——记贵州省五交化公司仓库

在社会化大生产的商品生产和流通过程中“储运”作为一个重要环节．如影随形般地与“商流”购销业务共存。商品运输实现了商品实体在购销之间的空间转移．商品储存解决了商品实体在购销之间的时间延续．而“商流”则是商品以货币为媒介实现商品收购与销售在不同商品所有者之间所有权转移的传递过程。     

商检基金会——德国消费者的好参谋

在德国的乡村和城市,各类专业商店、超级市场比比皆是,商店里商品琳琅满目,顾客购物时有足够的选择余地。但顾客有时会遇到一个问题:在商品的海洋里,不知买哪种最好。设在柏林的德国商品检测基金会和它编辑出版的《检测》杂志成了消费者最好的导购参谋。 商品检测基金会新闻处的法拉克小姐告诉笔者,该基金成立于1964年,是一个民间组织,80％经费来自基     

人工神经网络技术在物流需求量预测中的应用

运用人工神经网络模拟人脑的思维过程,建立了多个变量之间的非线性模型,从而为准确预测物流需求量提供了科学的依据。     

基于灰色神经网络组合模型的废旧产品回收预测

制造企业对废旧产品进行回收再利用,是节约资源和保护环境的有效方式。由于在产品回收过程中存在诸多不确定性因素,如何对产品回收量进行有效预测是亟待解决的关键问题。本文针对废旧产品回收的特点,将灰色GM（1,1）模型与BP神经网络预测方法结合,构建了灰色神经网络组合预测模型对产品回收量进行预测。该组合模型兼具两种预测方法的优点,预测效果优于各独立模型。案例分析表明了该种预测方法的有效性。     

基于改进型BP神经网络模型的机场物流吐量预测研究

对BP神经网络模型进行了改进，并用于机场物流吞吐量的预测，通过运用桂林机场的实际数据进行检验分析，证明改进后的模型在对机场物流吞吐量和旅客吞吐量的预测上均具有较高的准确性和敏捷性。     

Carbon option price forecasting based on modified fractional Brownian motion optimized by GARCH model in carbon emission trading

With the rapid growth of carbon trading, the development of carbon financial derivatives such as carbon options has become inevitable. This paper established a model based on GARCH and fractional Brownian motion (FBM), hoping to provide reference for China's upcoming carbon option trading through carbon option price forecasting research. The fractal characteristic of carbon option prices indicates that it is reasonable to use FBM to predict option prices. The GARCH model can make up for the lack of fixed FBM volatility. In this paper, the daily closing prices of EUA option contracts on the European Energy Exchange are selected as samples for price prediction. The GARCH model was used to determine the return volatility, and then the FBM was used to calculate the forecast price for the next 60 days. The results showed that the predicted price can better fit the actual price. This paper further compares the price prediction results of this model with the other three models through line graphs and error evaluation indicators such as MAPE, MAE and MSE. It is confirmed that the prediction results of the model in this paper is the closest to the actual price.     

Modelling the iron ore price index: A new perspective from a hybrid data reconstructed EEMD-GORU model

As iron ore is the fundamental steel production resource, predicting its price is strategically important for risk management at related enterprises and projects. Based on a signal decomposition technology and an artificial neural network, this paper proposes a hybrid EEMD-GORU model and a novel data reconstruction method to explore the price risk and fluctuation correlations between China’s iron ore futures and spot markets, and to forecast the price index series of China’s and international iron ore spot markets from the futures market. The analysis found that the iron ore futures market in China better reflected the price fluctuations and risk factors in the imported and international iron ore spot markets. However, the forward price in China’s iron ore futures market was unable to adequately reflect the changes in the domestic iron ore market, and was therefore unable to fully disseminate domestic iron ore market information. The proposed model was found to provide better market risk perceptions and predictions through its combinations of the different volatility information in futures and spot markets. The results are valuable references for the early-warning and management of the related enterprise project risks.     

Volatility forecasting in the Bitcoin market: A new proposed measure based on the VS-ACARR approach

This paper proposes a new volatility-spillover-asymmetric conditional autoregressive range (VS-ACARR) approach that takes into account the intraday information, the volatility spillover from crude oil as well as the volatility asymmetry (leverage effect) to model/forecast Bitcoin volatility (price range). An empirical application to Bitcoin and crude oil (WTI) price ranges shows the existence of strong volatility spillover from crude oil to the Bitcoin market and a weak leverage effect in the Bitcoin market. The VS-ACARR model yields higher forecasting accuracy than the GARCH, CARR, and VS-CARR models regarding out-of-sample forecast performance, suggesting that accounting for the volatility spillover and asymmetry can significantly improve the forecasting accuracy of Bitcoin volatility. The superior forecast performance of the VS-ACARR model is robust to alternative out-of-sample forecast windows. Our findings highlight the importance of accommodating intraday information, spillover from crude oil, and volatility asymmetry in forecasting Bitcoin volatility.     

考虑市场力的短期边际电价预测

电价波动较负荷波动剧烈,使得整个电价的预测精度降低。造成这种价格波动的主要原因是由于在电力市场中,发电商拥有的市场力具有能够支配电价上下波动的能力,使得电价的变化更加难以预测。因此市场力在电价预测中是必须考虑的重要因素之一。提出将市场供需比指标作为电价预测的一个输入量,将其引入到预测模型中作为影响电价的因素,使预测精度得到提高。     

第三产业产值比重预测的BP神经网络模型

宏观经济政策的制定要参照各次产业的发展水平,所以对产业发展水平进行评估和预测就显得十分必要。通过运用改进BP算法神经网络的建模方法对我国第三产业产值比重进行分析,最后建立了单隐层的BP神经网络模型。结果表明基于改进BP算法的神经网络模型预测精度高,收敛速度快,具有良好的泛化能力,模型的通用性和实用性强。     

论连锁店的分店补货模型

本文采用实证的方法,对连锁店分店的自动补货模型进行了分析,并研究其对降低连锁店分店库存水平的作用.首先,阐述了连锁店分店库存的重要性,分析了连锁店分店常用的补货模型;分析销量的预测方法：然后通过实证分析模拟人工智能的补货公式,如何实现连锁店分店的全自动补货功能.     

Residual value forecasting using asymmetric cost functions

Leasing is a popular channel for marketing new cars. However, the pricing of leases is complicated because the leasing rate must embody an expectation of the car’s residual value after contract expiration. This paper develops resale price forecasting models in order to aid pricing decisions. One feature of the leasing business is that different forecast errors entail different costs. The primary objective of this paper is to identify effective ways of addressing cost asymmetry. Specifically, this paper contributes to the literature by (i) consolidating prior work in forecasting on asymmetric functions of the cost of errors; (ii) systematically evaluating previous approaches and comparing them to a new approach; and (iii) demonstrating that forecasting using asymmetric cost of error functions improves the quality of decision support in car leasing. For example, if the costs of overestimating resale prices are twice those of underestimating them, incorporating cost asymmetry into forecast model development reduces costs by about 8%.