Mean-variance model for fuzzy capital budgeting

In an uncertain economic environment, it is usually difficult to predict accurately the investment outlays and annual net cash flows of a project. In addition, available investment capital sometimes cannot be accurately given either. Fuzzy variables can reflect vagueness of these parameters. In this paper, capital budgeting problem with fuzzy investment outlays, fuzzy annual net cash flows and fuzzy available investment capital is studied based on credibility measure. One new mean-variance model is proposed for optimal capital allocation. A fuzzy simulation-based genetic algorithm is provided for solving the proposed optimization problem. One numerical example and an experiment are also presented to show the optimization idea and the effectiveness of the algorithm.     

Discounted cash flow analysis of time series

The most precise models for treating risk and uncertainty in engineering economy require a knowledge of the covariances between future cash flows. Analysts have long been accustomed to developing expected values of future cash flows and some have started to obtain estimates of the variances of these quantities. Few have been willing to grapple with the formidable problems associated with quantitative examination of the covariances. In some cases the cash flows to be predicted may be considered to be future realizations of an established random process. In their book Time Series Analysis, Box and Jenkins (1970) have shown how to identify a model underlying the process providing that a historical record of sufficient length is available. They have also provided algorithms for machine calculation of (1) estimates of the model parameters, (2) unbiased minimum mean-square-error forecasts, and (3) variances of the forecasts of the future values of the process. The present paper extends the technique so as to obtain similar estimates of the covariance matrix. The present worth of the cash flow forecasts is their sum, weighted by the appropriate discount factors. The variance of the present worth is easily written in terms of the discount factors and the covariance function. A PL/I program is presented that accepts estimated model parameters and the historical record of the random cash flow process and then computes (1) the forecasts and their covariances, and (2) the present worth and its variance. Numerical results are presented by way of illustration.     

Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems

We present the results of our investigation into the use of genetic algorithms (GAs) for identifying near optimal design parameters of diagnostic systems that are based on artificial neural networks (ANNs) for condition monitoring of mechanical systems. ANNs have been widely used for health diagnosis of mechanical bearing using features extracted from vibration and acoustic emission signals. However, different sensors and the corresponding features exhibit varied response to different faults. Moreover, a number of different features can be used as inputs to a classifier ANN. Identification of the most useful features is important for an efficient classification as opposed to using all features from all channels, leading to very high computational cost and is, consequently, not desirable. Furthermore, determining the ANN structure is a fundamental design issue and can be critical for the classification performance. We show that a GA can be used to select a smaller subset of features that together form a genetically fit family for successful fault identification and classification tasks. At the same time, an appropriate structure of the ANN, in terms of the number of nodes in the hidden layer, can be determined, resulting in improved performance.     

减压渣油评价中的人工神经网络分析方法

介绍了渣油评价中人工神经分析方法,其目标是以少量的关键参数推演出渣油评价所需的并试图通过改变网络输入参数来确定不同性质指标对推演渣油其他参数的影响程度。文中给出了人工神经网络方法与多元线性回归方法的对比,表明人工神经网络用于模拟减压渣油的评价方法和实验过程的可行性,并为该方法用于仿真实验过程指出应用前景。     

Artificial neural network and wavelet neural network approaches for modelling of a solar air heater

This paper reports on a modelling study of new solar air heater (SAH) system by using artificial neural network (ANN) and wavelet neural network (WNN) models. In this study, a device for inserting an absorbing plate made of aluminium cans into the double-pass channel in a flat-plate SAH. A SAH system is a multi-variable system that is hard to model by conventional methods. As regards the ANN and WNN methods, it has a superior capability for generalization, and this capability is independent on the dimensionality of the input data’s. In this study, an ANN and WNN based methods were intended to adopt SAH system for efficient modelling. To evaluate prediction capabilities of different types of neural network models (ANN and WNN), their best architecture and effective training parameters should be found. The performance of the proposed methodology was evaluated by using several statistical validation parameters. Comparison between predicted and experimental results indicates that the proposed WNN model can be used for estimating the some parameters of SAHs with reasonable accuracy.     

Learning optimization for CPN-based training in robot positioning control

Artificial neural net (ANN) models have been applied to the inverse kinematic problem for controlling robot positions. The selection of ANN training parameters, however, is an important yet complicated step which has to be taken before an ANN model for robot positioning control can be implemented effectively. The objective of this research is to utilize the counterpropagation network (CPN) for inverse kinematic mapping and obtain the best performance possible by systematic adjustment of network parameters. Taguchi statistical methods, efficient methods for analyzing the capability and accuracy of a system, have been used in this study. The working envelope of the robot simulated in this research is 150×150×60 mm³. The optimal accuracy and standard deviation determined by this research are 2.62 mm and 1.2 mm, respectively.     

A flexible neural network-fuzzy mathematical programming algorithm for improvement of oil price estimation and forecasting

This paper presents a flexible algorithm based on artificial neural network (ANN) and fuzzy regression (FR) to cope with optimum long-term oil price forecasting in noisy, uncertain, and complex environments. The oil supply, crude oil distillation capacity, oil consumption of non-OECD, USA refinery capacity, and surplus capacity are incorporated as the economic indicators. Analysis of variance (ANOVA) and Duncan’s multiple range test (DMRT) are then applied to test the significance of the forecasts obtained from ANN and FR models. It is concluded that the selected ANN models considerably outperform the FR models in terms of mean absolute percentage error (MAPE). Moreover, Spearman correlation test is applied for verification and validation of the results. The proposed flexible ANN–FR algorithm may be easily modified to be applied to other complex, non-linear and uncertain datasets.     

基于人工神经网络组合预测油田产量

油田原油产量的准确预测可以对油田的生产管理进行合理的指导。该文探讨了应用神经网络组合方法预测油田产量，对开井数、含水率、动用储量以及往年产量同未来产量之间的复杂关系建立模型。采用了两层预测系统：第一层包含两个神经网络，一个多层前馈网络和一个函数链接网络；第二层是把第一层的两个网络输出进行组合。研究了五种不同的组合算法：平均法、最小平方回归法、模糊逻辑法、自适应前馈神经网络法和自适应函数链接神经网络法。根据油品类型分为稀油、热采稠油、常规稠油和总产量四组数据，对上述方法进行了测试，结果表明应用人工神经网络的组合预测方法优于其他的预测方法，而且适用范围广。     

Using online cognitive tasks to predict mathematics low school achievement

This study aimed to analyze the validity of an online cognitive screening battery to predict mathematic school achievement using artificial neural networks (ANNs). The tasks were designed to measure; selective attention, visuo-spatial working memory, mental rotation, and arithmetic ability in an online, game-like format. In the first study, we investigated the cognitive performance of students with low and typical achievement in mathematics and language. In the second study, we developed an ANN to classify mathematics school achievement. Finally, we tested the adequacy of this network to classify an unknown sample to the ANN. Most of the performance differences in the battery were related to mathematics achievement. The ANN was able to predict mathematics achievement with acceptable accuracy and presented equivalent results in a simulation involving a different sample. We suggest that this assessment model combining ANNs and online cognitive tasks may be a valuable tool to research low school achievement in school settings.     

Artificial neural network modelling in simulation of complex flow at open channel junctions based on large data sets

The flow characteristics in open channel junctions are of great interest in hydraulic and environmental engineering areas. This study investigates the capacity of artificial neural network (ANN) models for representing and modelling the velocity distributions of combined open channel flows. ANN models are constructed and tested using data derived from computational-fluid-dynamics models. The orthogonal sampling method is used to select representative data. The ANN models trained and validated by representative data generally outperform those by using random data. Sobols' sensitivity analysis is performed to investigate contributions of different uncertainty sources to model performance. Results indicate that the major uncertainty source is from ANN model parameter initialization. Hence an ANN model training strategy is designed in order to reduce the main uncertainty source: models are trained for many runs with random model parameter initializations and the model with the best performance is adopted.     

基于群盲签名的离线的公平电子现金方案

电子支付具有传统支付方式(现金、支票、信用卡等)的优点,同时克服了传统货币的一些缺陷,成为电子商务的核心技术和关键环节。近年来,人们提出了许多不同的密码体制用于构造电子现金系统(在线的或离线的)但这些方案要么效率不高,要么安全性能不够好。为了克服这些缺陷,提出了一种有效的基于群(?)签名的离线电子现金系统。该方案能很好地满足效率和安全需求,同时能很好的解决电子现金中遇到的二次付费问题。     

Degasification system selection for US longwall mines using an expert classification system

Methane emissions from the active face areas and from the fractured formations overlying the mined coalbed can affect safety and productivity in longwall mines. Since ventilation alone may not be sufficient to control the methane levels on a longwall operation, gob vent boreholes (GVB), horizontal and vertical drainage boreholes, and their combinations are drilled and used as supplementary methane control measures in many mines. However, in most cases, the types of degasification wellbores chosen are decided based on previous experiences without analyzing the different factors that may affect this decision.This study describes the development of an expert classification system used as a decision tool. It was built using a multilayer perceptron (MLP) type artificial neural network (ANN) structure. The ANN was trained using different geographical locations, longwall operation parameters, and coalbed characteristics as input and was tested to classify the output into four different selections, which are actual degasification designs that US longwall mines utilize. The ANN network selected no degasification, GVB, horizontal and GVB, and horizontal, vertical and GVB options with high accuracy. The results suggest that the model can be used as a decision tool for degasification system selection using site- and mine-specific conditions. Such a model can also be used as a screening tool to decide which degasification design should be investigated in detail with more complex numerical techniques.     

Molecular dynamics simulation of nanoscale liquid flows

Molecular dynamics (MD) simulation is a powerful tool to investigate the nanoscale fluid flow. In this article, we review the methods and the applications of MD simulation in liquid flows in nanochannels. For pressure-driven flows, we focus on the fundamental research and the rationality of the model hypotheses. For electrokinetic-driven flows and the thermal-driven flows, we concentrate on the principle of generating liquid motion. The slip boundary condition is one of the marked differences between the macro- and micro-scale flows and the nanoscale flows. In this article, we review the parameters controlling the degree of boundary slip and the new findings. MD simulation is based on the Newton's second law to simulate the particles' interactions and consists of several important processing methods, such as the thermal wall model, the cut-off radius, and the initial condition. Therefore, we also reviewed the recent improvement in these key methods to make the MD simulation more rational and efficient. Finally, we summarized the important discoveries in this research field and proposed some worthwhile future research directions.     

基于小波分解和RBF网络的三极管电路故障诊断

在分析现有三极管放大电路故障诊断算法不足的基础上,提出了一种基于模型的小波-RBF网络故障诊断算法。在PSPICE环境下建立三极管常见的故障模型,利用多层小波分解优异的时频特性提取故障特征参数,利用RBF强大的非线性分类能力和快速的收敛特性进行了典型共基极放大电路中三极管的软、硬故障诊断仿真。计算及仿真结果显示,这种故障诊断算法具有诊断速度快、诊断正确率高的特点。     

人工神经网络在EPDM硫化胶性能预测中的应用

该文将人工神经网络方法应用于EPDM硫化胶的性能预测，用按回归通用旋转组合设计方法设计的EPDM硫化胶20次性能试验数据作为人工神经网络的样本数据，利用MATLAB 6．5软件包中的神经网络工具箱，构造BP神经网络，优选最佳的神经网络参数，通过训练后，用于预测EPDM硫化胶的氧指数、扯断强度和伸长率性能。结果表明，训练好的神经网络可准确地预测EPDM硫化胶的有关性能，基于MATLAB 6．5的人工神经网络是分析EPDM配方各组分对硫化胶性能影响的一种快捷、可靠的新方法。     

An artificial neural network based decision support system for energy efficient ship operations

Reducing fuel consumption of ships against volatile fuel prices and greenhouse gas emissions resulted from international shipping are the challenges that the industry faces today. The potential for fuel savings is possible for new builds, as well as for existing ships through increased energy efficiency measures; technical and operational respectively. The limitations of implementing technical measures increase the potential of operational measures for energy efficient ship operations. Ship owners and operators need to rationalise their energy use and produce energy efficient solutions. Reducing the speed of the ship is the most efficient method in terms of fuel economy and environmental impact. The aim of this paper is twofold: (i) predict ship fuel consumption for various operational conditions through an inexact method, Artificial Neural Network ANN; (ii) develop a decision support system (DSS) employing ANN-based fuel prediction model to be used on-board ships on a real time basis for energy efficient ship operations. The fuel prediction model uses operating data – ‘Noon Data’ – which provides information on a ship’s daily fuel consumption. The parameters considered for fuel prediction are ship speed, revolutions per minute (RPM), mean draft, trim, cargo quantity on board, wind and sea effects, in which output data of ANN is fuel consumption. The performance of the ANN is compared with multiple regression analysis (MR), a widely used surface fitting method, and its superiority is confirmed. The developed DSS is exemplified with two scenarios, and it can be concluded that it has a promising potential to provide strategic approach when ship operators have to make their decisions at an operational level considering both the economic and environmental aspects.     

基于车型识别的BP算法Matlab实现

车型识别具有广阔的应用前景,BP神经网络在车型识别中能够提高车型的识别率。在任何车型大致都可以抽象成一个"工"字型情况下,提取其中的顶长比、前后比和顶高比这三项相对参数作为BP神经网络的输入参数。采用三层3-8-3的BP神经网络,并用14对输入参数离线训练,再用4对新数据进行检验,均得到了预想的期望值。     

A meta-heuristic framework for forecasting household electricity consumption

It may be difficult to model household electricity consumption with conventional methods such as regression due to seasonal and monthly changes. This paper illustrates a flexible integrated meta-heuristic framework based on Artificial Neural Network (ANN) Multi Layer Perceptron (MLP), conventional regression and design of experiment (DOE) for forecasting household electricity consumption. Previous studies base their verification by the difference in error estimation, whereas this study uses various error estimation methods and design of experiment (DOE). Moreover, DOE is based on analysis of variance (ANOVA) and Duncan Multiple Range Test (DMRT). Furthermore, actual data is compared with ANN MLP and conventional regression model through ANOVA. If the null hypothesis is accepted, DMRT is used to select either ANN MLP or conventional regression. However, if the null hypothesis is accepted then the proposed framework selects either the MLP or regression model based on the average of Minimum Absolute Percentage Error (MAPE), Mean Square Error (MSE) and Mean Absolute Error (MAE). The significance of this study is the integration of ANN MLP, conventional regression and DOE for flexible modeling and improved processing, development and testing of household electricity consumption. Some of the previous studies assume that ANN MLP provide better estimation and others estimate electricity consumptions based on the conventional regression approach. However, this study presents a flexible integrated framework to locate the best model based on the actual data. Moreover, it would provide more reliable and precise forecasting for policy makers. To show the applicability and superiority of the integrated approach, annual household electricity consumption in Iran from 1974 to 2003 was collected for processing, training and testing purpose.     

一种基于证据理论与神经网络的遥感影像分类方法

把影像的空间信息融入分类决策，提出了一种基于证据理论与神经网络的遥感影像分类方法。对原图像作平滑处理．得到原图像的平滑图像；利用神经网络对原图像及其平滑图像分别进行训练、分类；利用证据理论对它们的分类结果(决策)进行融合；最后，把融合结果(决策)作为原图像的最终分类结果。实验结果与性能比较表明．新方法是有效的．提高了影像的分类精度。     

Application of partial mutual information variable selection to ANN forecasting of water quality in water distribution systems

Recent trends in the management of water supply have increased the need for modelling techniques that can provide reliable, efficient, and accurate representation of the complex, non-linear dynamics of water quality within water distribution systems. Statistical models based on artificial neural networks (ANNs) have been found to be highly suited to this application, and offer distinct advantages over more conventional modelling techniques. However, many practitioners utilise somewhat heuristic or ad hoc methods for input variable selection (IVS) during ANN development.This paper describes the application of a newly proposed non-linear IVS algorithm to the development of ANN models to forecast water quality within two water distribution systems. The intention is to reduce the need for arbitrary judgement and extensive trial-and-error during model development. The algorithm utilises the concept of partial mutual information (PMI) to select inputs based on the analysis of relationship strength between inputs and outputs, and between redundant inputs. In comparison with an existing approach, the ANN models developed using the IVS algorithm are found to provide optimal prediction with significantly greater parsimony. Furthermore, the results obtained from the IVS procedure are useful for developing additional insight into the important relationships that exist between water distribution system variables.