基于非齐次指数函数的灰色预测模型背景值重构方法研究

灰色模型背景值重构是从变形数据本身出发,考虑数据波动性而引起的模型偏差对预测结果的影响,对背景值的构造形式加以改进。本文针对齐次指数函数的背景值拟合法存在的不足,提出了一种基于非齐次指数函数的背景值重构方法。其核心思想是充分考虑u/a的影响,将原始数据的1-AGO序列抽象为非齐次指数函数。该方法建模简单,计算方便,能够有效地提高拟合、预测精度。且不受0<-a<2的限制,在-a≥2的情况下也能保持良好的拟合、预测效果。

基于拟牛顿法确定参数的时间序列迭代模型

为解决时间序列参数估计收敛速度慢,迭代次数多,效率低的问题,建立了一种基于拟牛顿法的参数估计方法。实例表明,由此所建立的时间序列迭代模型稳定可靠,拟合、预测精度高,能够有效的表征形变量随时间的变化规律。

一种基于BP神经网络的尾矿坝沉降预报方法

基于BP神经网络建立尾矿坝沉降预报模型,重点对BP神经网络的拓扑结构和学习算法进行研究。并以某尾矿库初期坝的沉降监测数据为例,对模型的拟合、预测精度进行验证。实例表明,BP神经网络自学习、自组织能力强,具有极强的线性逼真能力,能够准确地反映输入、输出变量之间的非线性关系,有效地表征尾矿坝的沉降变形规律,对即将发生的变形情况做出科学、合理的预报。

Analysis of radar fault prediction based on combined model

Based on modeling principle of GM（1,1）model and linear regression model,a combined prediction model is established to predict equipment fault by the fitting of two models.The new prediction model takes full advantage of prediction information provided by the two models and improves the prediction precision.Finally,this model is introduced to predict the system fault time according to the output voltages of a certain type of radar transmitter.

Prediction of Injection-Production Ratio with BP Neural Network

Injection of water to enhance oil production is commonplace, and improvements in understanding the process are economically important. This study examines predictive models of the injection-to-production ratio. Firstly, the error between the fitting and actual injection-production ratio is calculated with such methods as the injection-production ratio and water-oil ratio method, the material balance method, the multiple regression method, the gray theory GM (1,1) model and the back-propogation (BP) neural network method by computer applications in this paper. The relative average errors calculated are respectively 1.67%, 1.08%, 19.2%, 1.38% and 0.88%. Secondly, the reasons for the errors from different prediction methods are analyzed theoretically, indicating that the prediction precision of the BP neural network method is high, and that it has a better self-adaptability, so that it can reflect the internal relationship between the injection-production ratio and the influencing factors. Therefore, the BP neural network method is suitable to the prediction of injection-production ratio.

New Keynesian Model and Inflation Prediction

Recently, much dispute has occurred about the validity of the New Keynesian model from both theoretical and empirical views. However, a few studies have analyzed this model from the empirical perspective. Few other studies have analyzed this model directly or nearly directly. This article empirically examines the New Keynesian model focusing on inflation forecast. Generalized method of moments （GMM） is used to examine whether the typical Keynesian model with Taylor rule is empirically appropriate for the US, UK, Euro area, and Japan. The results showed： （1） the New Keynesian model fits well in most cases and explains the real economy well. Taylor rule also fits well for most cases; （2） Rational expectations for inflation rates may not be useful based on this model When AR （1） （time series analysis） model is used to capture inflation expectations instead of one-time ahead real values, the model fits well. With measured expectations, the relative weight of the forward-looking terms increase on the cost of lagged inflation and output terms, even up to the point at which the lagged inflation terms are no longer needed to rescue the new Keynesian model; and （3） Forward-looking variables play more important roles than backward-looking ones in economic activity. Results with expectations with forward-looking terms perform better in general. This New Keynesian model may reduce the importance of lagged output in some cases

Correlating and Predicting the Solubilities of Solid n-Alkanes in Supercritical Ethane Using Carnahan-Starling-van der Waals Model

The solubilities of some solid n-alkanes in supercritical ethane were correlated and predicted in this paper using the Carnahan-Starling-van der Waals model with a density-dependent parameter of a12.At a given temperature,the linear fit of the parameter of a12and the density of the supercritical solvent was used for solubility correlation,resulting in an average absolute average relative deviation(AARD)of 8.68%,which was between the values of the semiempirical models and the other compressed gas models used in this article.In the linear fit of the parameter of a12and the density of the solvent,the regressed slope m and intercept n of the linear fit can be correlated with the carbon atom number of solid n-alkanes and then the solubilities of solid n-alkanes in supercritical ethane can be predicted with the intercept n and slope m.The average AARD in solubility prediction was 26.99%.

Computational Simulation to Predict the Syngas Composition Produced during Charcoal Gasification

Charcoal gasification could mitigate the energetic problems in the rural zones since these regions have considerable amounts of wood, which is the base of such a fuel available. This paper presents some CFD （computational fluid dynamic） predictions of the experimental results obtained from the fixed bed gasification of charcoal made in a pilot-scale downdraft reactor using air, which was designed and built by the Research Group in Clean Development Mechanisms and Energy Management, from the National University of Colombia. The quality of the syngas obtained from the process was evaluated through the CO and CO2 percentages measured in its composition. The performance at various air flow rates （measured at the system entrance, through an analog flow meter） is evaluated with the help of 11 thermocouples, which give the information to create a temperature profile, and three load cells to measure the solid fuel conversion rate. To simulate the process, the information from temperature profile, charcoal proximate analysis, air flow meter and load cells were taken as inputs and the syngas composition was obtained as the result from the calculation. The domain was defined as 2D with an axis-symmetric description, using quads as mesh elements. The calculation and results were performed in a CFD commercial code widely used for this type of simulations： ANSYS FLUENT. The predictions made by the software were validated with the experimental results obtained in the laboratory.

The Effect of Integrating Inductive Approach and Deductive Approach With Multimedia Assistance Into Acquisition of Subjunctive Mood

This study applied both inductive approach and deductive approach with multimedia assistance into an English grammar class on the acquisition of subjunctive mood. It aimed to investigate whether this kind of teaching approach, as a general grammar pedagogy, would improve the efficiency of students＇ acquisition of certain grammar points. This study results from comparison and contrast between one same class on the acquisition of subjunctive mood to 70 students in total, employing three different teaching methods： inductive approach with multimedia assistance （method 1）, deductive approach with multimedia assistance （method 2）, and both inductive approach and deductive approach with multimedia assistance （method 3）, based on self-reported reflection on the experiment, observation of students＇ learning process, students＇ pre-test and after-class evaluation test results, and surveys. Two identical experiments were conducted to two groups of students of different levels of language proficiency to increase the generalizability of the results. Findings revealed that the evaluation test score of the grammatical points taught with method 3 is much higher than those taught with the other two methods, and most students felt positive about method 3. Students nevertheless encounter obvious difficulties in inductive approach, indicating certain lack of self-learning skills in Chinese students

Effects of Probability of Accurately Predicting and Ascertaining Defection on Cooperation and its Application to Simulated Internet Dealing

Without cooperative behaviors, our society or organization falls into social dilemma situations where every member selects uncooperative （defective） behaviors, and the situation gets worse and worse. Such a situation in a society or an organization leads to violation of social or organizational rules, and at the worst case it suffers from serious accidents or scandals. Therefore, it is important for us to make efforts and take measures to elicit cooperative behaviors. It was demonstrated theoretically that altruism strategy and adaptive prediction and ascertainment strategy are in some cases better than rational strategy under the situation of social dilemma. We built up a mathematical model in order to examine how the probability of correctly predicting and ascertaining the behavior （cooperation or defection） of opponents and the mixture of （a） altruism （all cooperation） strategy, （b） individualism （all defection） strategy, and （c） adaptive prediction and ascertainment strategy affected the expected profit. Simulation results showed that the tit-for-tat strategy was better than the rational （individualism） strategy when the probability of correctly predicting and ascertaining defection of the opponent was considerably higher. As an application of the basic study above, it was explored, using a simulation method, how such a system as opening reputation or peer review in public could work satisfactorily to prevent defective behaviors in auction dealing. The result showed that the information on the handle name and the reputation effectively worked to prevent defective behaviors.

Electric Energy Management Modeling for Kingdom of Bahrain

In the deregulated economy, the maximum load forecasting is important for the electric industry. Many applications are included such as the energy generation and purchasing. The aim of the present study is to find the most suitable models for the peak load of the Kingdom of Bahrain. Many mathematical methods have been developing for maximum load forecasting. In the present paper, the modeling of the maximum load, population and GDP （gross domestic product） versus years obtained. The curve fitting technique used to find that models, where Graph 4.4.2 as a tool used to find the models. As well, Neuro-Fuzzy used to find the three models. Therefore, three techniques are used. These three are exponential, linear modeling and Neuro-Fuzzy. It is found that, the Neuro-Fuzzy is the most suitable and realistic one. Then, the linear modeling is the next suitable one.

Forecasting Gas Consumption Based on a Residual Auto-Regression Model and Kalman Filtering Algorithm

Consumption of clean energy has been increasing in China.Forecasting gas consumption is important to adjusting the energy consumption structure in the future.Based on historical data of gas consumption from 1980 to 2017,this paper presents a weight method of the inverse deviation of fitted value,and a combined forecast based on a residual auto-regression model and Kalman filtering algorithm is used to forecast gas consumption.Our results show that:(1)The combination forecast is of higher precision:the relative errors of the residual auto-regressive model,the Kalman filtering algorithm and the combination model are within the range(–0.08,0.09),(–0.09,0.32)and(–0.03,0.11),respectively.(2)The combination forecast is of greater stability:the variance of relative error of the residual auto-regressive model,the Kalman filtering algorithm and the combination model are 0.002,0.007 and 0.001,respectively.(3)Provided that other conditions are invariant,the predicted value of gas consumption in 2018 is 241.81×10~9 m^3.Compared to other time-series forecasting methods,this combined model is less restrictive,performs well and the result is more credible.

Study on Dynamical Behavior of Turbulent Premixed V-flames

Differential interference contrast method was applied to investigate the flickering characteristics of V-flame in terms of both temporal and spatial scales. The flow characteristics of incident premixed combustibles were measured by means of laser Doppler anemometry (LDA). The characteristics of wake flow behind the stabilizing rod were obtained with numerical simulation. While the integral temporal scale of incident turbulence is about 1ms, the integral spatial scale about 2 mm, the characteristic flickering time scale is about 83 ms and spatial scale about 10 mm. The flame flickering frequency is more than 12 Hz, does not comply with the wake vortex shedding frequency, which is about 110 Hz. It is well-known that the flickering of V-flame is affected by both the incident turbulence and the wake flow after the stabilizing rod, but the result of the paper shows that the relationship between flickering characteristics and flow characteristics is much more complicated by the chemical reaction.